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paperID stringlengths 36 36	pwc_id stringlengths 8 47	arxiv_id stringlengths 6 16 ⌀	nips_id float64	url_abs stringlengths 18 329	url_pdf stringlengths 18 742	title stringlengths 8 325	abstract stringlengths 1 7.27k ⌀	authors stringlengths 2 7.06k	published stringlengths 10 10 ⌀	conference stringlengths 12 47 ⌀	conference_url_abs stringlengths 16 198 ⌀	conference_url_pdf stringlengths 27 199 ⌀	proceeding stringlengths 6 47 ⌀	taskID stringlengths 7 1.44k	areaID stringclasses 688 values	embedding stringlengths 9.26k 12.5k	umap_embedding stringlengths 29 44
91de2389-e4fa-47a8-b1d8-e711c5f1c68f	neural-concept-formation-in-knowledge-graphs	null	null	https://openreview.net/forum?id=V61-62OS4mZ	https://openreview.net/pdf?id=V61-62OS4mZ	Neural Concept Formation in Knowledge Graphs	In this work, we investigate how to learn novel concepts in Knowledge Graphs (KGs) in a principled way, and how to effectively exploit them to produce more accurate neural link prediction models. Specifically, we show how concept membership relationships learned via unsupervised clustering of entities can be reified and used to augment a KG. In a thorough set of experiments, we confirm that neural link predictors trained on these augmented KGs, or in a joint Expectation-Maximization iterative scheme, can generalize better and produce more accurate predictions for infrequent relationships. For instance, our method yields relative improvements of up to 8.6% MRR on WN18RR for rare predicates, and up to 82% in small-data regimes, where the model has access to just a small subset of the training triples. Furthermore, our proposed models are able to learn meaningful concepts.	['Pasquale Minervini', 'Antonio Vergari', 'Agnieszka Dobrowolska']	2021-06-22	null	null	null	akbc-2021-10	['novel-concepts']	['reasoning']	[ 1.54296324e-01 8.67769837e-01 -6.80537403e-01 -6.45391524e-01 -2.36760721e-01 -3.32420886e-01 3.21055084e-01 4.21708882e-01 -3.44073683e-01 1.20781672e+00 1.37622237e-01 -2.46026158e-01 -3.62160653e-01 -1.14378023e+00 -1.16703248e+00 -1.55001879e-01 -5.35619795e-01 7.03670561e-01 2.03727037e-01 -1.64004818e-01 -3.04270357e-01 3.12875748e-01 -1.26758432e+00 1.52926981e-01 1.31287313e+00 7.20617175e-01 -1.09426551e-01 2.76628315e-01 -7.17689320e-02 8.86690259e-01 -1.94362357e-01 -7.91421711e-01 -3.12800333e-02 -6.36593476e-02 -1.04167628e+00 -3.78318071e-01 4.07629371e-01 -1.51912794e-01 -4.75584954e-01 9.76112962e-01 9.73426402e-02 4.17182267e-01 6.79234982e-01 -9.45565283e-01 -1.05270863e+00 1.45925033e+00 -5.18380880e-01 3.15076470e-01 1.30442619e-01 -4.22742695e-01 1.61547422e+00 -9.14651453e-01 9.03729916e-01 1.07008874e+00 7.71530569e-01 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5657ebcc-0485-4be6-9c31-8ca3fb8c111a	a-large-scale-study-of-language-models-for	1804.01849	null	http://arxiv.org/abs/1804.01849v1	http://arxiv.org/pdf/1804.01849v1.pdf	A Large-Scale Study of Language Models for Chord Prediction	We conduct a large-scale study of language models for chord prediction. Specifically, we compare N-gram models to various flavours of recurrent neural networks on a comprehensive dataset comprising all publicly available datasets of annotated chords known to us. This large amount of data allows us to systematically explore hyper-parameter settings for the recurrent neural networks---a crucial step in achieving good results with this model class. Our results show not only a quantitative difference between the models, but also a qualitative one: in contrast to static N-gram models, certain RNN configurations adapt to the songs at test time. This finding constitutes a further step towards the development of chord recognition systems that are more aware of local musical context than what was previously possible.	['Filip Korzeniowski', 'David R. W. Sears', 'Gerhard Widmer']	2018-04-05	null	null	null	null	['chord-recognition']	['audio']	[ 1.31269753e-01 -1.49378553e-01 -1.12563297e-02 -5.72638437e-02 -6.83169484e-01 -1.00801635e+00 3.60530078e-01 -4.51352932e-02 -5.17974138e-01 4.59509373e-01 5.72915971e-01 -2.67934382e-01 -1.70638099e-01 -6.52473748e-01 -2.88768828e-01 -5.65615356e-01 -3.11165273e-01 6.61789775e-01 5.04404008e-01 -7.02310681e-01 4.10270900e-01 5.75588405e-01 -1.48328602e+00 4.05647784e-01 -7.73190930e-02 5.12066960e-01 1.17409259e-01 1.04943848e+00 2.16723934e-01 8.93398046e-01 -5.56805134e-01 -2.02781036e-01 2.07842022e-01 -7.14278162e-01 -9.40511882e-01 -4.52200830e-01 1.72453314e-01 2.57465690e-01 -3.03477645e-01 7.09769487e-01 7.43931890e-01 4.58440155e-01 5.56505919e-01 -4.78914171e-01 -3.54028970e-01 1.25539529e+00 -1.11388443e-02 3.21812689e-01 1.89961195e-01 2.43172765e-01 1.46057296e+00 -5.38937986e-01 7.32476175e-01 8.33171487e-01 1.01066327e+00 6.32754445e-01 -1.08668840e+00 -6.31997168e-01 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1.90137908e-01 1.03192382e-01 -1.49010435e-01 -1.97172523e-01 -1.08706973e-01]	[15.893318176269531, 5.330941200256348]
6bb64aa4-f278-433d-8d44-b75d3ffadc49	consistent-and-symmetry-preserving-data	2104.11578	null	https://arxiv.org/abs/2104.11578v1	https://arxiv.org/pdf/2104.11578v1.pdf	Consistent and symmetry preserving data-driven interface reconstruction for the level-set method	Recently, machine learning has been used to substitute parts of conventional computational fluid dynamics, e.g. the cell-face reconstruction in finite-volume solvers or the curvature computation in the Volume-of-Fluid (VOF) method. The latter showed improvements in terms of accuracy for coarsely resolved interfaces, however at the expense of convergence and symmetry. In this work, a combined approach is proposed, adressing the aforementioned shortcomings. We focus on interface reconstruction (IR) in the level-set method, i.e. the computation of the volume fraction and apertures. The combined model consists of a classification neural network, that chooses between the conventional (linear) IR and the neural network IR depending on the local interface resolution. The proposed approach improves accuracy for coarsely resolved interfaces and recovers the conventional IR for high resolutions, yielding first order overall convergence. Symmetry is preserved by mirroring and rotating the input level-set grid and subsequently averaging the predictions. The combined model is implemented into a CFD solver and demonstrated for two-phase flows. Furthermore, we provide details of floating point symmetric implementation and computational efficiency.	['Nikolaus Adams', 'Deniz A. Bezgin', 'Aaron B. Buhendwa']	2021-04-23	null	null	null	null	['face-reconstruction']	['computer-vision']	[ 1.12194330e-01 -1.17852084e-01 4.91536885e-01 2.18671620e-01 -6.04186475e-01 -1.84181243e-01 6.49611294e-01 3.24491858e-01 -3.71626735e-01 9.88221288e-01 -2.43738443e-01 -3.24923217e-01 -3.40482146e-01 -9.80118155e-01 -4.43558455e-01 -1.04268193e+00 1.76164676e-02 7.28078723e-01 1.59679070e-01 -2.62490511e-01 3.82438898e-01 8.94627273e-01 -1.89849782e+00 3.27333897e-01 1.10450327e+00 1.22653198e+00 -2.15366483e-01 6.90595329e-01 -3.64773512e-01 6.21974707e-01 -8.50257427e-02 1.42021403e-01 2.05133572e-01 -2.13170886e-01 -6.50380492e-01 -1.56142637e-01 4.37544197e-01 -1.95397720e-01 2.90798634e-01 5.66363811e-01 4.33526844e-01 3.89648944e-01 9.61053610e-01 -7.73768306e-01 1.29567146e-01 -1.65334642e-02 -3.96267146e-01 -9.27667171e-02 3.99685472e-01 -3.48245562e-03 5.72766006e-01 -1.27787817e+00 5.43397427e-01 8.96344304e-01 9.03654635e-01 2.14743987e-01 -1.34385800e+00 -1.31554723e-01 -3.64412695e-01 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3963eb53-5252-41fe-a220-3e7e72c7c72f	resources-and-evaluations-for-multi	2306.12601	null	https://arxiv.org/abs/2306.12601v1	https://arxiv.org/pdf/2306.12601v1.pdf	Resources and Evaluations for Multi-Distribution Dense Information Retrieval	We introduce and define the novel problem of multi-distribution information retrieval (IR) where given a query, systems need to retrieve passages from within multiple collections, each drawn from a different distribution. Some of these collections and distributions might not be available at training time. To evaluate methods for multi-distribution retrieval, we design three benchmarks for this task from existing single-distribution datasets, namely, a dataset based on question answering and two based on entity matching. We propose simple methods for this task which allocate the fixed retrieval budget (top-k passages) strategically across domains to prevent the known domains from consuming most of the budget. We show that our methods lead to an average of 3.8+ and up to 8.0 points improvements in Recall@100 across the datasets and that improvements are consistent when fine-tuning different base retrieval models. Our benchmarks are made publicly available.	['Simran Arora', 'Omar Khattab', 'Soumya Chatterjee']	2023-06-21	null	null	null	null	['retrieval', 'question-answering', 'information-retrieval']	['methodology', 'natural-language-processing', 'natural-language-processing']	[-2.10464269e-01 -5.10327697e-01 -5.26404142e-01 -2.33647972e-01 -1.98825192e+00 -1.10904813e+00 6.78387940e-01 5.10692894e-01 -5.34735560e-01 9.06216025e-01 1.99299380e-01 -8.69033709e-02 -6.29670799e-01 -7.66481400e-01 -6.29947305e-01 -3.76926154e-01 1.20171323e-01 1.33626032e+00 6.35550082e-01 -3.41625720e-01 4.74821061e-01 5.02526760e-01 -1.44645298e+00 5.30076087e-01 6.28048897e-01 9.49928164e-01 3.17754522e-02 8.14154923e-01 -3.53760511e-01 6.21657252e-01 -1.09526861e+00 -2.37133235e-01 4.58187640e-01 -4.30445597e-02 -1.02265215e+00 -5.20315230e-01 6.89613879e-01 -5.95242500e-01 -6.05933547e-01 7.11037576e-01 6.96214318e-01 4.30305600e-01 1.08636510e+00 -1.09436989e+00 -9.92352009e-01 3.66921127e-01 -6.86262429e-01 5.66915810e-01 6.70047998e-01 -4.42831308e-01 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b328b38b-0cbc-44e0-b008-7896e324eaa0	chili-pepper-disease-diagnosis-via-image	2306.12057	null	https://arxiv.org/abs/2306.12057v1	https://arxiv.org/pdf/2306.12057v1.pdf	Chili Pepper Disease Diagnosis via Image Reconstruction Using GrabCut and Generative Adversarial Serial Autoencoder	With the recent development of smart farms, researchers are very interested in such fields. In particular, the field of disease diagnosis is the most important factor. Disease diagnosis belongs to the field of anomaly detection and aims to distinguish whether plants or fruits are normal or abnormal. The problem can be solved by binary or multi-classification based on CNN, but it can also be solved by image reconstruction. However, due to the limitation of the performance of image generation, SOTA's methods propose a score calculation method using a latent vector error. In this paper, we propose a network that focuses on chili peppers and proceeds with background removal through Grabcut. It shows high performance through image-based score calculation method. Due to the difficulty of reconstructing the input image, the difference between the input and output images is large. However, the serial autoencoder proposed in this paper uses the difference between the two fake images except for the actual input as a score. We propose a method of generating meaningful images using the GAN structure and classifying three results simultaneously by one discriminator. The proposed method showed higher performance than previous researches, and image-based scores showed the best performanc	['Sungyoung Kim', 'Jongwook Si']	2023-06-21	null	null	null	null	['image-reconstruction', 'anomaly-detection']	['computer-vision', 'methodology']	[ 3.03958982e-01 -3.16793501e-01 1.62073508e-01 -1.66590855e-01 -6.73410371e-02 -2.61246473e-01 1.68067962e-01 -1.79791704e-01 2.76662922e-03 4.86118883e-01 -4.02705550e-01 6.00404851e-02 -1.01132356e-01 -1.41629064e+00 -4.16858345e-01 -1.01171851e+00 3.96085948e-01 1.16422191e-01 6.36012927e-02 -2.81823158e-01 1.10325798e-01 4.16846305e-01 -1.43413019e+00 3.09709191e-01 1.06688607e+00 1.09913659e+00 4.15244251e-01 4.76284802e-01 -4.45358843e-01 6.64755881e-01 -1.15306151e+00 -2.56817043e-01 3.49206924e-01 -1.20515418e+00 -3.62644255e-01 1.76843092e-01 -4.52688336e-02 -6.44359231e-01 3.48820090e-02 1.43946350e+00 4.71086919e-01 -3.73298347e-01 6.93641007e-01 -1.46339977e+00 -1.11075723e+00 4.51759696e-01 -6.32218838e-01 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c13f7897-3384-4256-85c6-222f39ed7c89	channel-recurrent-attention-networks-for	2010.03108	null	https://arxiv.org/abs/2010.03108v1	https://arxiv.org/pdf/2010.03108v1.pdf	Channel Recurrent Attention Networks for Video Pedestrian Retrieval	Full attention, which generates an attention value per element of the input feature maps, has been successfully demonstrated to be beneficial in visual tasks. In this work, we propose a fully attentional network, termed {\it channel recurrent attention network}, for the task of video pedestrian retrieval. The main attention unit, \textit{channel recurrent attention}, identifies attention maps at the frame level by jointly leveraging spatial and channel patterns via a recurrent neural network. This channel recurrent attention is designed to build a global receptive field by recurrently receiving and learning the spatial vectors. Then, a \textit{set aggregation} cell is employed to generate a compact video representation. Empirical experimental results demonstrate the superior performance of the proposed deep network, outperforming current state-of-the-art results across standard video person retrieval benchmarks, and a thorough ablation study shows the effectiveness of the proposed units.	['Mehrtash Harandi', 'Lars Petersson', 'Jieming Zhou', 'Pan Ji', 'Pengfei Fang']	2020-10-07	null	null	null	null	['person-retrieval']	['computer-vision']	[ 3.31758559e-01 -4.68979299e-01 -1.05405629e-01 -6.46921322e-02 -6.49204075e-01 -2.54998449e-02 6.67910695e-01 -3.15437496e-01 -3.57983410e-01 5.90136170e-01 6.06874585e-01 1.86678290e-01 1.51827484e-01 -5.35718501e-01 -8.61878991e-01 -7.90900230e-01 -8.10200050e-02 -2.36137047e-01 9.76259857e-02 5.31656183e-02 1.66045710e-01 1.85503915e-01 -1.78323710e+00 6.54671311e-01 5.30960917e-01 1.37397957e+00 4.62384909e-01 7.65930533e-01 1.72042683e-01 1.27195692e+00 -5.97364604e-01 -2.40849674e-01 1.72969699e-02 -2.99013674e-01 -5.15770674e-01 -1.24579743e-01 4.61369187e-01 -7.20947623e-01 -1.12005746e+00 7.20463574e-01 7.06781924e-01 4.39777046e-01 8.03683400e-01 -8.37787747e-01 -1.27053511e+00 4.55318987e-01 -5.71735203e-01 8.28846335e-01 5.04759789e-01 2.21892580e-01 1.13395238e+00 -1.27254629e+00 4.42217529e-01 1.35545611e+00 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1.00181259e-01 -9.06694949e-01 7.54621476e-02 4.60671633e-01 4.31188345e-01 -9.65963542e-01 2.87881851e-01 2.42348164e-01]	[9.441946983337402, 0.6835160255432129]
07343c04-d472-4117-93fd-aaedd6793ec2	can-neural-networks-do-arithmetic-a-survey-on	2303.07735	null	https://arxiv.org/abs/2303.07735v1	https://arxiv.org/pdf/2303.07735v1.pdf	Can neural networks do arithmetic? A survey on the elementary numerical skills of state-of-the-art deep learning models	Creating learning models that can exhibit sophisticated reasoning skills is one of the greatest challenges in deep learning research, and mathematics is rapidly becoming one of the target domains for assessing scientific progress in this direction. In the past few years there has been an explosion of neural network architectures, data sets, and benchmarks specifically designed to tackle mathematical problems, reporting notable success in disparate fields such as automated theorem proving, numerical integration, and discovery of new conjectures or matrix multiplication algorithms. However, despite these impressive achievements it is still unclear whether deep learning models possess an elementary understanding of quantities and symbolic numbers. In this survey we critically examine the recent literature, concluding that even state-of-the-art architectures often fall short when probed with relatively simple tasks designed to test basic numerical and arithmetic knowledge.	['Alberto Testolin']	2023-03-14	null	null	null	null	['numerical-integration', 'automated-theorem-proving', 'automated-theorem-proving']	['miscellaneous', 'miscellaneous', 'reasoning']	[-1.94684893e-01 -1.03545956e-01 -2.23597452e-01 -2.23596275e-01 -2.01922163e-01 -6.41880989e-01 7.34340131e-01 5.51803887e-01 -4.35138553e-01 8.27689946e-01 -2.87558585e-01 -9.73234534e-01 -4.46500242e-01 -1.21110022e+00 -7.15499222e-01 -2.55844891e-01 -3.75246882e-01 5.26835799e-01 -2.19003975e-01 -5.23171842e-01 5.66715717e-01 7.35024750e-01 -1.34258640e+00 8.90057236e-02 8.95370960e-01 1.14068627e+00 -5.63145936e-01 7.82502770e-01 -3.47819000e-01 1.35976529e+00 -5.87597668e-01 -6.86285436e-01 2.14038014e-01 -3.53685528e-01 -9.71815825e-01 -8.58779728e-01 5.89076579e-01 -3.99743825e-01 -5.18593788e-01 1.15479875e+00 8.26887861e-02 6.68514818e-02 3.96888673e-01 -1.49970329e+00 -8.68238091e-01 7.50834882e-01 -2.18655810e-01 5.37041664e-01 1.54518858e-01 2.58585483e-01 1.23982167e+00 -4.63477015e-01 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372e1a7f-7a3a-4cdf-af48-6fd0413ca8d8	pac-assisted-value-factorisation-with	2206.11420	null	https://arxiv.org/abs/2206.11420v3	https://arxiv.org/pdf/2206.11420v3.pdf	PAC: Assisted Value Factorisation with Counterfactual Predictions in Multi-Agent Reinforcement Learning	Multi-agent reinforcement learning (MARL) has witnessed significant progress with the development of value function factorization methods. It allows optimizing a joint action-value function through the maximization of factorized per-agent utilities due to monotonicity. In this paper, we show that in partially observable MARL problems, an agent's ordering over its own actions could impose concurrent constraints (across different states) on the representable function class, causing significant estimation error during training. We tackle this limitation and propose PAC, a new framework leveraging Assistive information generated from Counterfactual Predictions of optimal joint action selection, which enable explicit assistance to value function factorization through a novel counterfactual loss. A variational inference-based information encoding method is developed to collect and encode the counterfactual predictions from an estimated baseline. To enable decentralized execution, we also derive factorized per-agent policies inspired by a maximum-entropy MARL framework. We evaluate the proposed PAC on multi-agent predator-prey and a set of StarCraft II micromanagement tasks. Empirical results demonstrate improved results of PAC over state-of-the-art value-based and policy-based multi-agent reinforcement learning algorithms on all benchmarks.	['Vaneet Aggarwal', 'Tian Lan', 'Hanhan Zhou']	2022-06-22	null	null	null	null	['starcraft-ii']	['playing-games']	[ 9.33378178e-04 3.37637067e-01 -7.03318775e-01 -7.28595704e-02 -1.04379749e+00 -4.86216396e-01 7.58728504e-01 1.25225976e-01 -8.62132728e-01 1.56500614e+00 4.47000980e-01 -1.93858057e-01 -4.48424280e-01 -7.45422244e-01 -8.85571718e-01 -9.10247803e-01 -6.83379650e-01 7.03703523e-01 -2.49959201e-01 -3.14699680e-01 1.89516563e-02 -3.92066725e-02 -1.31687689e+00 3.90387863e-01 7.54361808e-01 9.52060044e-01 8.90948176e-02 6.61737859e-01 3.20014536e-01 1.33430743e+00 -6.44056678e-01 -4.94902045e-01 5.52560925e-01 -3.72807205e-01 -5.86950481e-01 -1.54029906e-01 -1.58371627e-01 -9.40099418e-01 -2.42287472e-01 9.04352486e-01 4.39851731e-01 4.73658800e-01 6.73738301e-01 -1.83149004e+00 -3.37780595e-01 1.17881715e+00 -5.31198561e-01 -1.75321057e-01 2.00210989e-01 6.36239350e-01 1.39208126e+00 4.43545468e-02 5.32073319e-01 1.66264749e+00 2.60085315e-01 8.45506847e-01 -1.45269680e+00 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86976613-7bf1-456b-82c5-d500533d2921	monocular-3d-object-detection-using-multi	2212.11804	null	https://arxiv.org/abs/2212.11804v1	https://arxiv.org/pdf/2212.11804v1.pdf	Monocular 3D Object Detection using Multi-Stage Approaches with Attention and Slicing aided hyper inference	3D object detection is vital as it would enable us to capture objects' sizes, orientation, and position in the world. As a result, we would be able to use this 3D detection in real-world applications such as Augmented Reality (AR), self-driving cars, and robotics which perceive the world the same way we do as humans. Monocular 3D Object Detection is the task to draw 3D bounding box around objects in a single 2D RGB image. It is localization task but without any extra information like depth or other sensors or multiple images. Monocular 3D object detection is an important yet challenging task. Beyond the significant progress in image-based 2D object detection, 3D understanding of real-world objects is an open challenge that has not been explored extensively thus far. In addition to the most closely related studies.	['Ashish Patel', 'Abonia Sojasingarayar']	2022-12-22	null	null	null	null	['monocular-3d-object-detection']	['computer-vision']	[ 9.89828184e-02 -1.63990825e-01 2.02761710e-01 -2.35028028e-01 9.24237967e-02 -7.84826398e-01 4.96305585e-01 -2.50436049e-02 -4.58533257e-01 2.91924417e-01 -5.47745168e-01 -4.77302819e-01 4.83384699e-01 -6.86647594e-01 -6.26309335e-01 -3.28630507e-01 1.65992066e-01 5.87148786e-01 9.19364214e-01 -2.90166825e-01 3.80129874e-01 1.23715281e+00 -1.79464543e+00 -2.30604485e-01 1.47022709e-01 1.24738967e+00 5.61934471e-01 9.15202320e-01 -6.19707219e-02 2.75320232e-01 -3.07790279e-01 -1.01208396e-01 4.97306108e-01 2.72824485e-02 -1.29995018e-01 3.38278443e-01 4.68838543e-01 -6.34278953e-01 -3.99475634e-01 1.03968060e+00 1.94326997e-01 -9.45656840e-03 4.10432816e-01 -1.28548002e+00 -3.42275441e-01 -4.74266976e-01 -7.74313807e-01 6.98203743e-02 8.45731854e-01 4.99429330e-02 2.40629956e-01 -9.33380306e-01 6.13104343e-01 1.38215435e+00 2.00562581e-01 4.14714187e-01 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252dfae8-3c79-4859-8731-65362d70fa17	towards-a-better-understanding-of	2305.18491	null	https://arxiv.org/abs/2305.18491v1	https://arxiv.org/pdf/2305.18491v1.pdf	Towards a Better Understanding of Representation Dynamics under TD-learning	TD-learning is a foundation reinforcement learning (RL) algorithm for value prediction. Critical to the accuracy of value predictions is the quality of state representations. In this work, we consider the question: how does end-to-end TD-learning impact the representation over time? Complementary to prior work, we provide a set of analysis that sheds further light on the representation dynamics under TD-learning. We first show that when the environments are reversible, end-to-end TD-learning strictly decreases the value approximation error over time. Under further assumptions on the environments, we can connect the representation dynamics with spectral decomposition over the transition matrix. This latter finding establishes fitting multiple value functions from randomly generated rewards as a useful auxiliary task for representation learning, as we empirically validate on both tabular and Atari game suites.	['Rémi Munos', 'Yunhao Tang']	2023-05-29	null	null	null	null	['value-prediction']	['computer-code']	[ 3.80673148e-02 2.57456988e-01 -7.59773910e-01 -1.18984714e-01 -8.37758243e-01 -7.46941507e-01 5.37485898e-01 3.18429887e-01 -4.77282286e-01 1.03467464e+00 3.78732532e-01 -5.25980055e-01 -5.67838490e-01 -5.89721203e-01 -7.18707085e-01 -5.01070082e-01 -6.04621530e-01 4.44654077e-01 -6.87963970e-04 -5.53710878e-01 3.91249061e-01 2.79594153e-01 -1.45029783e+00 1.58292890e-01 6.62259221e-01 8.60214949e-01 -1.50202572e-01 7.26712406e-01 1.90846041e-01 1.40294504e+00 -5.05558014e-01 -4.22862284e-02 4.02510583e-01 -4.99859959e-01 -8.95516455e-01 -1.01493932e-02 -2.99152702e-01 -6.48896754e-01 -5.93281746e-01 7.77311206e-01 4.19166237e-01 6.81637347e-01 7.72844434e-01 -1.27633119e+00 -3.55051637e-01 8.64426851e-01 -2.72892684e-01 2.79180557e-01 4.01785046e-01 5.51923931e-01 1.16538286e+00 -1.65450647e-01 6.82727873e-01 1.19862902e+00 6.75402522e-01 6.14339888e-01 -1.57255006e+00 -3.23363513e-01 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a4d27304-421c-46e7-8f4e-b6cd0beaa69a	multi-modal-page-stream-segmentation-with	null	null	https://link.springer.com/article/10.1007/s10579-019-09476-2	https://www.inf.uni-hamburg.de/en/inst/ab/lt/publications/2019-wiedemann-lre-pss.pdf	Multi-modal Page Stream Segmentation with Convolutional Neural Networks	In recent years, (retro-)digitizing paper-based files became a major undertaking for private and public archives as well as an important task in electronic mailroom applications. As first steps, the workflow usually involves batch scanning and optical character recognition (OCR) of documents. In the case of multi-page documents, the preservation of document contexts is a major requirement. To facilitate workflows involving very large amounts of paper scans, page stream segmentation (PSS) is the task to automatically separate a stream of scanned images into coherent multi-page documents. In a digitization project together with a German federal archive, we developed a novel approach for PSS based on convolutional neural networks (CNN). As a first project, we combine visual information from scanned images with semantic information from OCR-ed texts for this task. The multi-modal combination of features in a single classification architecture allows for major improvements towards optimal document separation. Further to multimodality, our PSS approach profits from transfer-learning and sequential page modeling. We achieve accuracy up to 95% on multi-page documents on our in-house dataset and up to 93% on a publicly available dataset.	['Gerhard Heyer', 'Gregor Wiedemann']	2019-09-27	null	null	null	lang-resources-evaluation-2019-9	['page-stream-segmentation']	['natural-language-processing']	[ 6.70916975e-01 -1.95391372e-01 1.54130861e-01 -2.95114279e-01 -1.16281581e+00 -8.68752360e-01 5.69551170e-01 4.09916759e-01 -4.00897682e-01 3.82297307e-01 -6.19797818e-02 -3.81673992e-01 -2.39493340e-01 -6.03458226e-01 -6.65960968e-01 -2.46021464e-01 3.54760557e-01 8.67896736e-01 3.68163407e-01 1.07595190e-01 5.19239783e-01 1.10770488e+00 -1.40636790e+00 9.17419136e-01 7.19632030e-01 8.65691960e-01 6.16234779e-01 1.15006709e+00 -8.91650319e-01 3.25680375e-01 -6.82069123e-01 -4.61209953e-01 1.03707209e-01 -2.37383887e-01 -1.15249753e+00 6.73698127e-01 7.37189949e-01 -2.41813689e-01 -1.31252892e-02 7.99045146e-01 2.35415891e-01 -1.32422656e-01 6.04295015e-01 -8.07358027e-01 -5.24065316e-01 3.69088590e-01 -4.41428304e-01 -2.79318020e-02 1.53390259e-01 -1.13259152e-01 1.09169221e+00 -6.29075646e-01 7.20682383e-01 1.03781760e+00 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2.44239941e-01 -9.37988043e-01 -3.87596071e-01 -6.95858300e-01 -8.53534162e-01 3.03733498e-01 1.35111846e-02 -9.33669433e-02 -1.35943651e-01 4.04616207e-01]	[11.750812530517578, 2.6896584033966064]
31bf57ad-19aa-4903-96d6-71fe643559c7	video-face-clustering-with-unknown-number-of	1908.03381	null	https://arxiv.org/abs/1908.03381v2	https://arxiv.org/pdf/1908.03381v2.pdf	Video Face Clustering with Unknown Number of Clusters	Understanding videos such as TV series and movies requires analyzing who the characters are and what they are doing. We address the challenging problem of clustering face tracks based on their identity. Different from previous work in this area, we choose to operate in a realistic and difficult setting where: (i) the number of characters is not known a priori; and (ii) face tracks belonging to minor or background characters are not discarded. To this end, we propose Ball Cluster Learning (BCL), a supervised approach to carve the embedding space into balls of equal size, one for each cluster. The learned ball radius is easily translated to a stopping criterion for iterative merging algorithms. This gives BCL the ability to estimate the number of clusters as well as their assignment, achieving promising results on commonly used datasets. We also present a thorough discussion of how existing metric learning literature can be adapted for this task.	['Sanja Fidler', 'Marc T. Law', 'Makarand Tapaswi']	2019-08-09	video-face-clustering-with-unknown-number-of-1	http://openaccess.thecvf.com/content_ICCV_2019/html/Tapaswi_Video_Face_Clustering_With_Unknown_Number_of_Clusters_ICCV_2019_paper.html	http://openaccess.thecvf.com/content_ICCV_2019/papers/Tapaswi_Video_Face_Clustering_With_Unknown_Number_of_Clusters_ICCV_2019_paper.pdf	iccv-2019-10	['face-clustering']	['computer-vision']	[ 6.90754205e-02 -6.53285980e-02 -2.68651247e-01 -3.28140646e-01 -5.03036916e-01 -8.36068809e-01 5.81524193e-01 2.08750412e-01 -4.23652768e-01 3.41593415e-01 -1.22059703e-01 -7.69765377e-02 -1.24244347e-01 -5.36969364e-01 -5.45166552e-01 -7.11296618e-01 -1.84486344e-01 8.21935058e-01 1.97814584e-01 1.63019478e-01 2.17853025e-01 6.68225884e-01 -1.63628864e+00 -9.54759249e-04 4.91583139e-01 8.11958075e-01 -1.15332134e-01 6.89902425e-01 7.82606527e-02 6.90635562e-01 -3.14777851e-01 -7.44517744e-01 3.62934977e-01 -6.88437700e-01 -8.27109456e-01 4.73708838e-01 5.31078994e-01 -1.20429300e-01 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1fd2aa26-8c5c-4b6d-bea3-fb247190d80c	multi-view-subspace-clustering-via-partition	1912.01201	null	https://arxiv.org/abs/1912.01201v1	https://arxiv.org/pdf/1912.01201v1.pdf	Multi-view Subspace Clustering via Partition Fusion	Multi-view clustering is an important approach to analyze multi-view data in an unsupervised way. Among various methods, the multi-view subspace clustering approach has gained increasing attention due to its encouraging performance. Basically, it integrates multi-view information into graphs, which are then fed into spectral clustering algorithm for final result. However, its performance may degrade due to noises existing in each individual view or inconsistency between heterogeneous features. Orthogonal to current work, we propose to fuse multi-view information in a partition space, which enhances the robustness of Multi-view clustering. Specifically, we generate multiple partitions and integrate them to find the shared partition. The proposed model unifies graph learning, generation of basic partitions, and view weight learning. These three components co-evolve towards better quality outputs. We have conducted comprehensive experiments on benchmark datasets and our empirical results verify the effectiveness and robustness of our approach.	['Zenglin Xu', 'Boyu Wang', 'Zhao Kang', 'Juncheng Lv', 'Luping Ji']	2019-12-03	null	null	null	null	['multi-view-subspace-clustering']	['computer-vision']	[-1.57365635e-01 -5.27896643e-01 -1.45767763e-01 -5.70858158e-02 -5.12539804e-01 -7.55040526e-01 3.75528932e-01 7.99397826e-02 1.44503817e-01 2.03171283e-01 4.52410221e-01 1.74532682e-01 -4.09128547e-01 -7.21879840e-01 -1.36038601e-01 -9.43844259e-01 2.30492398e-01 2.18531981e-01 3.64398628e-01 1.33844435e-01 2.96466410e-01 1.69014886e-01 -1.37958312e+00 3.36331189e-01 1.04537046e+00 4.93329942e-01 1.54781461e-01 3.51318508e-01 8.63310508e-03 5.63896537e-01 -1.23103879e-01 -2.02345952e-01 1.27893493e-01 -4.28036213e-01 -5.78272879e-01 6.33885801e-01 -3.17890197e-01 2.20500290e-01 -7.89122954e-02 1.17281795e+00 6.05324626e-01 5.61744012e-02 6.88735545e-01 -1.39036250e+00 -3.46076429e-01 4.51104224e-01 -1.06953609e+00 5.72663322e-02 2.96456158e-01 -1.44384146e-01 1.06956291e+00 -8.87486994e-01 5.64091027e-01 1.24959540e+00 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e010b1af-75f1-40d7-8faa-1560020a6b0d	integration-of-workflow-and-pipeline-for	null	null	https://aclanthology.org/L14-1708	https://aclanthology.org/L14-1708.pdf	Integration of Workflow and Pipeline for Language Service Composition	Integrating language resources and language services is a critical part of building natural language processing applications. Service workflow and processing pipeline are two approaches for sharing and combining language resources. Workflow languages focus on expressive power of the languages to describe variety of workflow patterns to meet users{'} needs. Users can combine those language services in service workflows to meet their requirements. The workflows can be accessible in distributed manner and can be invoked independently of the platforms. However, workflow languages lack of pipelined execution support to improve performance of workflows. Whereas, the processing pipeline provides a straightforward way to create a sequence of linguistic processing to analyze large amounts of text data. It focuses on using pipelined execution and parallel execution to improve throughput of pipelines. However, the resulting pipelines are standalone applications, i.e., software tools that are accessible only via local machine and that can only be run with the processing pipeline platforms. In this paper we propose an integration framework of the two approaches so that each offests the disadvantages of the other. We then present a case study wherein two representative frameworks, the Language Grid and UIMA, are integrated.	['Trang Mai Xuan', 'Donghui Lin', 'Yohei Murakami', 'Toru Ishida']	2014-05-01	null	null	null	lrec-2014-5	['service-composition']	['miscellaneous']	[-2.33412459e-01 -9.92582738e-02 2.26164266e-01 -7.09883034e-01 -4.54200119e-01 -1.04721403e+00 7.60705411e-01 1.92833424e-01 -3.83341938e-01 1.70149565e-01 2.06915691e-01 -3.69870454e-01 -1.10844292e-01 -8.32143188e-01 9.92725790e-02 -4.15284008e-01 1.32810131e-01 5.96603572e-01 6.23472929e-01 -2.45969221e-01 -1.93176605e-02 8.12474012e-01 -1.83681953e+00 7.30973780e-01 8.87844861e-01 6.26631796e-01 6.23336852e-01 5.49136579e-01 -1.42791295e+00 6.63049221e-01 -3.03268969e-01 8.13262463e-02 2.49046355e-01 -1.06623515e-01 -9.22829390e-01 -5.75704314e-02 -4.86621439e-01 3.76107022e-02 4.80633557e-01 1.05390835e+00 3.71267617e-01 2.25271303e-02 6.13340884e-02 -1.66630030e+00 -1.22082524e-01 6.79158926e-01 -2.62348264e-01 -1.12170003e-01 7.82526433e-01 2.75452077e-01 5.71546555e-01 -8.04849088e-01 7.24574268e-01 1.27195668e+00 3.45390141e-01 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ee58f7bd-59ed-46da-abd6-b06771d80c57	a-min-max-cult-algorithm-for-graph	null	null	https://ieeexplore.ieee.org/document/989507	https://ieeexplore.ieee.org/document/989507	A Min-max Cult Algorithm for Graph Partitioning and Data Clustering	An important application of graph partitioning is data clustering using a graph model - the pairwise similarities between all data objects form a weighted graph adjacency matrix that contains all necessary information for clustering. In this paper, we propose a new algorithm for graph partitioning with an objective function that follows the min-max clustering principle. The relaxed version of the optimization of the min-max cut objective function leads to the Fiedler vector in spectral graph partitioning. Theoretical analyses of min-max cut indicate that it leads to balanced partitions, and lower bounds are derived. The min-max cut algorithm is tested on newsgroup data sets and is found to out-perform other current popular partitioning/clustering methods. The linkage-based refinements to the algorithm further improve the quality of clustering substantially. We also demonstrate that a linearized search order based on linkage differential is better than that based on the Fiedler vector, providing another effective partitioning method.	['Horst D. Simon', 'Ming Gu', 'Hongyuan Zhab', 'Xiaofeng He', 'Chris H.Q. Ding']	2002-08-07	null	null	null	proceedings-2001-ieee-international	['graph-partitioning']	['graphs']	[ 1.37864128e-01 2.79274970e-01 -5.26125193e-01 -4.26543415e-01 -3.39104950e-01 -7.52093494e-01 7.83955380e-02 4.92328078e-01 -1.29941404e-01 5.64762473e-01 -4.34471592e-02 -3.24611664e-01 -9.36588466e-01 -8.84025097e-01 -8.57959241e-02 -7.78700948e-01 -5.75620592e-01 9.67333674e-01 3.41747195e-01 -2.29957420e-02 3.70962143e-01 5.88701367e-01 -1.22353816e+00 8.38920549e-02 1.02719963e+00 3.48682970e-01 1.34478405e-01 6.96082056e-01 -1.24574192e-01 2.25424454e-01 -5.36403179e-01 -8.03070515e-02 5.23372173e-01 -7.01417506e-01 -1.16177857e+00 4.12243515e-01 6.71134219e-02 3.87043089e-01 -8.89024809e-02 1.09022629e+00 3.13190132e-01 2.61846542e-01 9.01499212e-01 -1.69504976e+00 -2.81443387e-01 9.36450541e-01 -1.05998504e+00 -5.23167066e-02 3.75002414e-01 -6.67000830e-01 1.25122082e+00 -3.42171997e-01 7.16847599e-01 1.19710624e+00 8.15652490e-01 -6.52601616e-03 -1.83331680e+00 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3.22557241e-01 -1.11422934e-01 -5.09195745e-01 -4.20119733e-01 1.69969648e-01]	[7.0391387939453125, 5.220965385437012]
d01b58b4-e14d-4a62-9907-274152cd5cb0	zerotop-zero-shot-task-oriented-semantic	2212.10815	null	https://arxiv.org/abs/2212.10815v1	https://arxiv.org/pdf/2212.10815v1.pdf	ZEROTOP: Zero-Shot Task-Oriented Semantic Parsing using Large Language Models	We explore the use of large language models (LLMs) for zero-shot semantic parsing. Semantic parsing involves mapping natural language utterances to task-specific meaning representations. Language models are generally trained on the publicly available text and code and cannot be expected to directly generalize to domain-specific parsing tasks in a zero-shot setting. In this work, we propose ZEROTOP, a zero-shot task-oriented parsing method that decomposes a semantic parsing problem into a set of abstractive and extractive question-answering (QA) problems, enabling us to leverage the ability of LLMs to zero-shot answer reading comprehension questions. For each utterance, we prompt the LLM with questions corresponding to its top-level intent and a set of slots and use the LLM generations to construct the target meaning representation. We observe that current LLMs fail to detect unanswerable questions; and as a result, cannot handle questions corresponding to missing slots. To address this problem, we fine-tune a language model on public QA datasets using synthetic negative samples. Experimental results show that our QA-based decomposition paired with the fine-tuned LLM can correctly parse ~16% of utterances in the MTOP dataset without requiring any annotated data.	['Subhro Roy', 'Jason Wolfe', 'Dheeraj Mekala']	2022-12-21	null	null	null	null	['semantic-parsing']	['natural-language-processing']	[ 6.59707963e-01 8.37527752e-01 1.37444377e-01 -7.29459643e-01 -1.51722038e+00 -5.82926393e-01 2.86325902e-01 1.56615317e-01 -1.69421718e-01 3.16875279e-01 3.73574644e-01 -5.45538485e-01 3.62763047e-01 -1.05304515e+00 -8.13742459e-01 -1.93772987e-01 3.34022641e-01 7.55775094e-01 4.13604617e-01 -5.11030376e-01 -3.32839675e-02 -4.50256526e-01 -1.51455700e+00 7.18401492e-01 1.03405106e+00 7.31368601e-01 5.91010392e-01 9.24199939e-01 -9.57000256e-01 1.03101790e+00 -7.31391430e-01 -5.75421989e-01 -1.34106338e-01 -6.90298617e-01 -1.35010672e+00 -8.50045010e-02 4.35983002e-01 -3.47065598e-01 1.31632805e-01 1.02125418e+00 2.91541427e-01 3.06214303e-01 3.71597409e-01 -1.08285367e+00 -8.27742398e-01 6.26198947e-01 -1.49202980e-02 2.79226094e-01 5.38630545e-01 1.41030520e-01 1.46204269e+00 -8.98939073e-01 7.69052804e-01 1.89851725e+00 3.52614373e-01 1.11248481e+00 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2d9d0368-6125-43aa-9038-3021a4dbe9bc	video-swin-transformer	2106.13230	null	https://arxiv.org/abs/2106.13230v1	https://arxiv.org/pdf/2106.13230v1.pdf	Video Swin Transformer	The vision community is witnessing a modeling shift from CNNs to Transformers, where pure Transformer architectures have attained top accuracy on the major video recognition benchmarks. These video models are all built on Transformer layers that globally connect patches across the spatial and temporal dimensions. In this paper, we instead advocate an inductive bias of locality in video Transformers, which leads to a better speed-accuracy trade-off compared to previous approaches which compute self-attention globally even with spatial-temporal factorization. The locality of the proposed video architecture is realized by adapting the Swin Transformer designed for the image domain, while continuing to leverage the power of pre-trained image models. Our approach achieves state-of-the-art accuracy on a broad range of video recognition benchmarks, including on action recognition (84.9 top-1 accuracy on Kinetics-400 and 86.1 top-1 accuracy on Kinetics-600 with ~20x less pre-training data and ~3x smaller model size) and temporal modeling (69.6 top-1 accuracy on Something-Something v2). The code and models will be made publicly available at https://github.com/SwinTransformer/Video-Swin-Transformer.	['Han Hu', 'Stephen Lin', 'Zheng Zhang', 'Yixuan Wei', 'Yue Cao', 'Jia Ning', 'Ze Liu']	2021-06-24	null	http://openaccess.thecvf.com//content/CVPR2022/html/Liu_Video_Swin_Transformer_CVPR_2022_paper.html	http://openaccess.thecvf.com//content/CVPR2022/papers/Liu_Video_Swin_Transformer_CVPR_2022_paper.pdf	cvpr-2022-1	['classification']	['methodology']	[-1.00641429e-01 -3.30111861e-01 -4.28591311e-01 -3.20141524e-01 -6.41346276e-01 -5.20365536e-01 8.55282545e-01 -5.00707030e-01 -5.84083378e-01 2.82659978e-01 4.83731031e-01 -3.50195289e-01 1.47634000e-01 -3.68176848e-01 -1.03781962e+00 -4.69646037e-01 -1.73182264e-02 1.42318949e-01 2.77123004e-01 -4.55942675e-02 6.91747591e-02 1.76029250e-01 -1.34463000e+00 9.00777280e-01 1.11251645e-01 1.34717429e+00 -1.50070503e-01 9.33331490e-01 1.83750167e-01 1.32120466e+00 -4.70945090e-02 -4.40401375e-01 2.90159285e-01 -6.96085468e-02 -8.51626277e-01 -2.30328627e-02 9.54655409e-01 -5.41124582e-01 -9.69075382e-01 6.47674799e-01 2.15638503e-01 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4526222b-d2f7-407f-b89e-6d00fe088338	skg-a-versatile-information-retrieval-and	2306.04758	null	https://arxiv.org/abs/2306.04758v1	https://arxiv.org/pdf/2306.04758v1.pdf	SKG: A Versatile Information Retrieval and Analysis Framework for Academic Papers with Semantic Knowledge Graphs	The number of published research papers has experienced exponential growth in recent years, which makes it crucial to develop new methods for efficient and versatile information extraction and knowledge discovery. To address this need, we propose a Semantic Knowledge Graph (SKG) that integrates semantic concepts from abstracts and other meta-information to represent the corpus. The SKG can support various semantic queries in academic literature thanks to the high diversity and rich information content stored within. To extract knowledge from unstructured text, we develop a Knowledge Extraction Module that includes a semi-supervised pipeline for entity extraction and entity normalization. We also create an ontology to integrate the concepts with other meta information, enabling us to build the SKG. Furthermore, we design and develop a dataflow system that demonstrates how to conduct various semantic queries flexibly and interactively over the SKG. To demonstrate the effectiveness of our approach, we conduct the research based on the visualization literature and provide real-world use cases to show the usefulness of the SKG. The dataset and codes for this work are available at https://osf.io/aqv8p/?view_only=2c26b36e3e3941ce999df47e4616207f.	['Han-Wei Shen', 'Rui Qiu', 'Yamei Tu']	2023-06-07	null	null	null	null	['knowledge-graphs', 'information-retrieval']	['knowledge-base', 'natural-language-processing']	[-4.57014233e-01 4.34032222e-03 -1.41657129e-01 -1.97841838e-01 -2.00019076e-01 -7.61417270e-01 5.21518826e-01 4.69442368e-01 -2.12866321e-01 7.47867942e-01 2.44628206e-01 -3.83464545e-01 -3.77521694e-01 -1.10113931e+00 -2.02128902e-01 -2.05172673e-01 2.47202843e-01 4.95801419e-02 5.16299307e-01 -7.45249689e-02 6.20612621e-01 5.24710894e-01 -1.81000555e+00 1.80015802e-01 1.20962465e+00 7.29854941e-01 6.34353518e-01 1.27882287e-01 -8.05085719e-01 3.59497160e-01 -8.62294316e-01 -4.75624561e-01 -1.12451866e-01 -1.56594649e-01 -1.05097187e+00 -4.12655652e-01 -2.60931045e-01 3.95526499e-01 -5.03957868e-02 1.20240974e+00 6.10096574e-01 5.62354065e-02 3.69279087e-01 -1.44022560e+00 -7.14542031e-01 5.48698008e-01 -3.02472621e-01 8.34027156e-02 6.61269844e-01 -2.39531204e-01 7.39785969e-01 -9.67927217e-01 1.04955971e+00 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266ccd50-8b56-4c06-b5a9-c52b89107d15	stacked-convolutional-and-recurrent-neural	1706.02292	null	http://arxiv.org/abs/1706.02292v1	http://arxiv.org/pdf/1706.02292v1.pdf	Stacked Convolutional and Recurrent Neural Networks for Music Emotion Recognition	This paper studies the emotion recognition from musical tracks in the 2-dimensional valence-arousal (V-A) emotional space. We propose a method based on convolutional (CNN) and recurrent neural networks (RNN), having significantly fewer parameters compared with the state-of-the-art method for the same task. We utilize one CNN layer followed by two branches of RNNs trained separately for arousal and valence. The method was evaluated using the 'MediaEval2015 emotion in music' dataset. We achieved an RMSE of 0.202 for arousal and 0.268 for valence, which is the best result reported on this dataset.	['Konstantinos Drossos', 'Roman Jarina', 'Sharath Adavanne', 'Miroslav Malik', 'Tuomas Virtanen', 'Dasa Ticha']	2017-06-07	null	null	null	null	['music-emotion-recognition']	['music']	[-1.35607094e-01 -9.60591733e-02 1.40792251e-01 -4.09924716e-01 -5.46347260e-01 -4.14343834e-01 1.00686394e-01 -6.55326396e-02 -6.46027029e-01 6.77783549e-01 3.35802376e-01 3.04565966e-01 -4.29260172e-03 -5.47177970e-01 -3.55128258e-01 -5.70135474e-01 -3.35905999e-01 -1.43490225e-01 -5.77729940e-01 -4.33920473e-01 1.11325338e-01 3.22346896e-01 -1.79775238e+00 5.09733856e-01 1.82266399e-01 1.78325832e+00 -5.41277349e-01 7.72112370e-01 -7.21881585e-03 9.88031805e-01 -7.33169198e-01 -4.91359860e-01 -1.01875179e-02 -3.42090130e-01 -6.23044550e-01 -5.81584334e-01 5.01247346e-02 3.05707008e-01 -1.10020652e-01 7.46253490e-01 7.54960895e-01 3.88685584e-01 4.73360091e-01 -1.07072866e+00 -5.20600438e-01 6.16282046e-01 -2.99188763e-01 -7.38549605e-02 2.06370458e-01 -3.34553242e-01 1.21025813e+00 -7.88739741e-01 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49545c8c-2279-4eef-8447-103cc6968b7e	elepose-unsupervised-3d-human-pose-estimation	2112.07088	null	https://arxiv.org/abs/2112.07088v1	https://arxiv.org/pdf/2112.07088v1.pdf	ElePose: Unsupervised 3D Human Pose Estimation by Predicting Camera Elevation and Learning Normalizing Flows on 2D Poses	Human pose estimation from single images is a challenging problem that is typically solved by supervised learning. Unfortunately, labeled training data does not yet exist for many human activities since 3D annotation requires dedicated motion capture systems. Therefore, we propose an unsupervised approach that learns to predict a 3D human pose from a single image while only being trained with 2D pose data, which can be crowd-sourced and is already widely available. To this end, we estimate the 3D pose that is most likely over random projections, with the likelihood estimated using normalizing flows on 2D poses. While previous work requires strong priors on camera rotations in the training data set, we learn the distribution of camera angles which significantly improves the performance. Another part of our contribution is to stabilize training with normalizing flows on high-dimensional 3D pose data by first projecting the 2D poses to a linear subspace. We outperform the state-of-the-art unsupervised human pose estimation methods on the benchmark datasets Human3.6M and MPI-INF-3DHP in many metrics.	['Helge Rhodin', 'James J. Little', 'Bastian Wandt']	2021-12-14	null	http://openaccess.thecvf.com//content/CVPR2022/html/Wandt_ElePose_Unsupervised_3D_Human_Pose_Estimation_by_Predicting_Camera_Elevation_CVPR_2022_paper.html	http://openaccess.thecvf.com//content/CVPR2022/papers/Wandt_ElePose_Unsupervised_3D_Human_Pose_Estimation_by_Predicting_Camera_Elevation_CVPR_2022_paper.pdf	cvpr-2022-1	['unsupervised-3d-human-pose-estimation']	['computer-vision']	[-1.33709326e-01 6.53824583e-02 -1.56642079e-01 -3.79246622e-01 -6.39857233e-01 -4.61197287e-01 4.02413577e-01 -3.97506356e-01 -7.89988399e-01 6.12680852e-01 5.14988840e-01 3.93399715e-01 4.10478443e-01 -2.96838880e-01 -8.58912051e-01 -3.57336581e-01 1.69439139e-04 1.08200753e+00 1.07925966e-01 -1.73367605e-01 -1.06635384e-01 4.06485587e-01 -1.22291172e+00 -2.06243724e-01 3.85407299e-01 5.87894440e-01 -2.69297920e-02 9.24280822e-01 3.28480482e-01 6.38516724e-01 -3.99486184e-01 -3.74929965e-01 5.89018703e-01 -4.36143309e-01 -7.85435379e-01 6.08980000e-01 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582e6961-aebc-487d-985b-d0fce2f2150e	neuroninspect-detecting-backdoors-in-neural	1911.07399	null	https://arxiv.org/abs/1911.07399v1	https://arxiv.org/pdf/1911.07399v1.pdf	NeuronInspect: Detecting Backdoors in Neural Networks via Output Explanations	Deep neural networks have achieved state-of-the-art performance on various tasks. However, lack of interpretability and transparency makes it easier for malicious attackers to inject trojan backdoor into the neural networks, which will make the model behave abnormally when a backdoor sample with a specific trigger is input. In this paper, we propose NeuronInspect, a framework to detect trojan backdoors in deep neural networks via output explanation techniques. NeuronInspect first identifies the existence of backdoor attack targets by generating the explanation heatmap of the output layer. We observe that generated heatmaps from clean and backdoored models have different characteristics. Therefore we extract features that measure the attributes of explanations from an attacked model namely: sparse, smooth and persistent. We combine these features and use outlier detection to figure out the outliers, which is the set of attack targets. We demonstrate the effectiveness and efficiency of NeuronInspect on MNIST digit recognition dataset and GTSRB traffic sign recognition dataset. We extensively evaluate NeuronInspect on different attack scenarios and prove better robustness and effectiveness over state-of-the-art trojan backdoor detection techniques Neural Cleanse by a great margin.	['Xijie Huang', 'Moustafa Alzantot', 'Mani Srivastava']	2019-11-18	null	null	null	null	['traffic-sign-recognition']	['computer-vision']	[ 9.13750008e-02 -1.77872241e-01 -1.72271222e-01 -2.26123512e-01 -3.15983772e-01 -8.07111382e-01 6.38121784e-01 -2.20628679e-02 -6.37624487e-02 3.33617955e-01 -1.38380930e-01 -8.55643153e-01 -7.72784203e-02 -5.61658800e-01 -1.12853277e+00 -8.15913200e-01 -1.19670860e-01 -2.25305989e-01 1.19397938e-01 -1.77170262e-02 4.73582566e-01 6.89807713e-01 -1.31600606e+00 6.60179019e-01 6.99047804e-01 8.07661176e-01 -7.11243033e-01 6.94610298e-01 1.89575441e-02 9.27577674e-01 -1.13962626e+00 -4.31878418e-01 4.79175419e-01 -2.86648422e-01 -1.58737436e-01 -5.30732214e-01 9.72613811e-01 -3.72298568e-01 -6.78359210e-01 1.45840240e+00 2.12040067e-01 -4.31891501e-01 5.40473521e-01 -1.88834083e+00 -6.14419460e-01 7.80584574e-01 -4.97426897e-01 5.22878468e-01 -1.16892554e-01 5.52359343e-01 7.17745960e-01 -5.96961558e-01 5.50361931e-01 1.15584719e+00 7.29598880e-01 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0b86aa19-fa9a-4093-8b54-8939814d6cad	safe-distributional-reinforcement-learning	2102.13446	null	https://arxiv.org/abs/2102.13446v1	https://arxiv.org/pdf/2102.13446v1.pdf	Safe Distributional Reinforcement Learning	Safety in reinforcement learning (RL) is a key property in both training and execution in many domains such as autonomous driving or finance. In this paper, we formalize it with a constrained RL formulation in the distributional RL setting. Our general model accepts various definitions of safety(e.g., bounds on expected performance, CVaR, variance, or probability of reaching bad states). To ensure safety during learning, we extend a safe policy optimization method to solve our problem. The distributional RL perspective leads to a more efficient algorithm while additionally catering for natural safe constraints. We empirically validate our propositions on artificial and real domains against appropriate state-of-the-art safe RL algorithms.	['Paul Weng', 'Jianyi Zhang']	2021-02-26	null	null	null	null	['distributional-reinforcement-learning']	['methodology']	[-6.21690117e-02 4.24771786e-01 -4.81295854e-01 -2.26854473e-01 -8.11249554e-01 -7.64069617e-01 7.11710632e-01 3.40077765e-02 -7.80415118e-01 1.23524857e+00 -2.78590024e-02 -5.82927704e-01 -3.47406358e-01 -7.37486124e-01 -8.97243679e-01 -7.63304949e-01 -4.14842993e-01 1.87525004e-02 3.05574715e-01 -2.52940178e-01 1.34605899e-01 4.84311998e-01 -1.52727079e+00 -4.32197630e-01 7.35834479e-01 9.35099125e-01 -2.83238113e-01 4.99789685e-01 5.24628222e-01 1.07936895e+00 -5.33098459e-01 -1.27741858e-01 3.60643804e-01 -2.56785601e-01 -7.30538905e-01 -3.27507138e-01 -1.31048337e-01 -4.07290906e-01 -1.55635223e-01 1.10492504e+00 3.52000326e-01 4.40985858e-01 5.93268216e-01 -1.80371583e+00 -9.74595994e-02 8.19773853e-01 -2.70021886e-01 -1.39190406e-01 2.34116390e-01 5.43339670e-01 9.38880205e-01 -3.93443182e-02 2.70013601e-01 1.21449447e+00 2.65597731e-01 9.84943390e-01 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0d522f4d-5258-4151-8e54-b38d76b3a7bf	localtrans-a-multiscale-local-transformer	2106.04067	null	https://arxiv.org/abs/2106.04067v2	https://arxiv.org/pdf/2106.04067v2.pdf	LocalTrans: A Multiscale Local Transformer Network for Cross-Resolution Homography Estimation	Cross-resolution image alignment is a key problem in multiscale gigapixel photography, which requires to estimate homography matrix using images with large resolution gap. Existing deep homography methods concatenate the input images or features, neglecting the explicit formulation of correspondences between them, which leads to degraded accuracy in cross-resolution challenges. In this paper, we consider the cross-resolution homography estimation as a multimodal problem, and propose a local transformer network embedded within a multiscale structure to explicitly learn correspondences between the multimodal inputs, namely, input images with different resolutions. The proposed local transformer adopts a local attention map specifically for each position in the feature. By combining the local transformer with the multiscale structure, the network is able to capture long-short range correspondences efficiently and accurately. Experiments on both the MS-COCO dataset and the real-captured cross-resolution dataset show that the proposed network outperforms existing state-of-the-art feature-based and deep-learning-based homography estimation methods, and is able to accurately align images under $10\times$ resolution gap.	['Yebin Liu', 'Ying Fu', 'Yuemei Zhou', 'Gaochang Wu', 'Ruizhi Shao']	2021-06-08	null	http://openaccess.thecvf.com//content/ICCV2021/html/Shao_LocalTrans_A_Multiscale_Local_Transformer_Network_for_Cross-Resolution_Homography_Estimation_ICCV_2021_paper.html	http://openaccess.thecvf.com//content/ICCV2021/papers/Shao_LocalTrans_A_Multiscale_Local_Transformer_Network_for_Cross-Resolution_Homography_Estimation_ICCV_2021_paper.pdf	iccv-2021-1	['homography-estimation']	['computer-vision']	[ 2.09018156e-01 -3.19905996e-01 2.32420370e-01 -2.95349449e-01 -1.17212856e+00 -4.31388050e-01 4.72535312e-01 -4.55060571e-01 -2.63230413e-01 3.83831590e-01 3.00048470e-01 3.82633567e-01 -4.42331403e-01 -7.54316747e-01 -1.04216504e+00 -5.95811486e-01 4.28676069e-01 4.33564425e-01 7.42652714e-02 -2.36595318e-01 3.62653375e-01 3.37629199e-01 -1.40729856e+00 1.24958545e-01 8.25100362e-01 1.05353498e+00 3.88111740e-01 3.71509433e-01 2.48772517e-01 3.90588164e-01 -1.45588174e-01 -4.20954466e-01 5.34198046e-01 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8d4e7cc7-0e5b-43a9-8890-81c1391b89e4	exemplar-bsaed-pattern-synthesis-with	2204.01671	null	https://arxiv.org/abs/2204.01671v2	https://arxiv.org/pdf/2204.01671v2.pdf	Exemplar-based Pattern Synthesis with Implicit Periodic Field Network	Synthesis of ergodic, stationary visual patterns is widely applicable in texturing, shape modeling, and digital content creation. The wide applicability of this technique thus requires the pattern synthesis approaches to be scalable, diverse, and authentic. In this paper, we propose an exemplar-based visual pattern synthesis framework that aims to model the inner statistics of visual patterns and generate new, versatile patterns that meet the aforementioned requirements. To this end, we propose an implicit network based on generative adversarial network (GAN) and periodic encoding, thus calling our network the Implicit Periodic Field Network (IPFN). The design of IPFN ensures scalability: the implicit formulation directly maps the input coordinates to features, which enables synthesis of arbitrary size and is computationally efficient for 3D shape synthesis. Learning with a periodic encoding scheme encourages diversity: the network is constrained to model the inner statistics of the exemplar based on spatial latent codes in a periodic field. Coupled with continuously designed GAN training procedures, IPFN is shown to synthesize tileable patterns with smooth transitions and local variations. Last but not least, thanks to both the adversarial training technique and the encoded Fourier features, IPFN learns high-frequency functions that produce authentic, high-quality results. To validate our approach, we present novel experimental results on various applications in 2D texture synthesis and 3D shape synthesis.	['Yajie Zhao', 'Shichen Liu', 'Weikai Chen', 'Jiayi Liu', 'Haiwei Chen']	2022-04-04	null	http://openaccess.thecvf.com//content/CVPR2022/html/Chen_Exemplar-Based_Pattern_Synthesis_With_Implicit_Periodic_Field_Network_CVPR_2022_paper.html	http://openaccess.thecvf.com//content/CVPR2022/papers/Chen_Exemplar-Based_Pattern_Synthesis_With_Implicit_Periodic_Field_Network_CVPR_2022_paper.pdf	cvpr-2022-1	['texture-synthesis']	['computer-vision']	[ 4.44726527e-01 2.78379083e-01 3.86488847e-02 3.56371611e-01 -1.98484182e-01 -5.71284652e-01 8.99032414e-01 -6.26550794e-01 4.87767011e-01 7.61758983e-01 1.31907701e-01 9.13763344e-02 -9.68853533e-02 -1.06377625e+00 -9.28482473e-01 -1.03968179e+00 8.08845237e-02 2.60280281e-01 -4.14854549e-02 -3.88529599e-01 1.16721697e-01 1.01443577e+00 -1.67047298e+00 1.90778986e-01 6.88809097e-01 1.18013179e+00 1.55822784e-01 5.30598700e-01 -1.71248063e-01 6.75485075e-01 -4.82048035e-01 -2.53727704e-01 4.05137330e-01 -6.52905166e-01 -3.43661755e-01 4.97884899e-01 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773755ef-044a-4de3-a853-29e3f17a8a0b	triplet-loss-based-embeddings-for-forensic	2102.12564	null	https://arxiv.org/abs/2102.12564v2	https://arxiv.org/pdf/2102.12564v2.pdf	Triplet loss based embeddings for forensic speaker identification in Spanish	With the advent of digital technology, it is more common that committed crimes or legal disputes involve some form of speech recording where the identity of a speaker is questioned [1]. In face of this situation, the field of forensic speaker identification has been looking to shed light on the problem by quantifying how much a speech recording belongs to a particular person in relation to a population. In this work, we explore the use of speech embeddings obtained by training a CNN using the triplet loss. In particular, we focus on the Spanish language which has not been extensively studies. We propose extracting the embeddings from speech spectrograms samples, then explore several configurations of such spectrograms, and finally, quantify the embeddings quality. We also show some limitations of our data setting which is predominantly composed by male speakers. At the end, we propose two approaches to calculate the Likelihood Radio given out speech embeddings and we show that triplet loss is a good alternative to create speech embeddings for forensic speaker identification.	['Ivan Meza', 'Carlos Mena', 'Javier Alvarez-Jimenez', 'Emmanuel Maqueda']	2021-02-24	null	null	null	null	['speaker-identification']	['speech']	[ 4.82294671e-02 3.05380225e-01 3.19742024e-01 -4.43130463e-01 -9.32079554e-01 -5.05957484e-01 8.46647859e-01 1.65427104e-01 -5.89861095e-01 5.35814464e-01 3.86510462e-01 -3.45686108e-01 -1.08735710e-02 -5.72766006e-01 -5.88576913e-01 -6.90141261e-01 6.69837520e-02 4.14901257e-01 -2.14888513e-01 1.52513608e-01 1.29041195e-01 6.06692731e-01 -1.38593793e+00 -7.64015615e-02 3.85963798e-01 9.38012838e-01 -2.53089517e-01 5.65445662e-01 -2.41278186e-01 3.70318294e-01 -1.02014756e+00 -1.04524124e+00 1.89140245e-01 -1.47668496e-01 -8.88191342e-01 1.00292869e-01 5.06373405e-01 -3.92784536e-01 -4.31836098e-01 1.21238005e+00 7.67288923e-01 6.97993189e-02 6.34646714e-01 -1.15555966e+00 -5.54578364e-01 9.12844598e-01 -2.64404625e-01 5.70408344e-01 4.41805094e-01 -1.13347910e-01 9.13718522e-01 -7.88011730e-01 4.56074029e-01 1.49269462e+00 5.65806985e-01 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6b1f1145-0c19-461b-958d-be9ca721a8e7	sparse-winograd-convolutional-neural-networks	1810.01973	null	http://arxiv.org/abs/1810.01973v1	http://arxiv.org/pdf/1810.01973v1.pdf	Sparse Winograd Convolutional neural networks on small-scale systolic arrays	The reconfigurability, energy-efficiency, and massive parallelism on FPGAs make them one of the best choices for implementing efficient deep learning accelerators. However, state-of-art implementations seldom consider the balance between high throughput of computation power and the ability of the memory subsystem to support it. In this paper, we implement an accelerator on FPGA by combining the sparse Winograd convolution, clusters of small-scale systolic arrays, and a tailored memory layout design. We also provide an analytical model analysis for the general Winograd convolution algorithm as a design reference. Experimental results on VGG16 show that it achieves very high computational resource utilization, 20x ~ 30x energy efficiency, and more than 5x speedup compared with the dense implementation.	['Song-Chun Zhu', 'Haochen Li', 'Feng Shi', 'Yuhe Gao', 'Benjamin Kuschner']	2018-10-03	null	null	null	null	['layout-design']	['computer-vision']	[-6.26766860e-01 -4.06499594e-01 -2.53311574e-01 -2.62697339e-01 3.32357556e-01 -8.28645751e-02 4.08602089e-01 1.54904678e-01 -5.01733720e-01 4.56718296e-01 1.94817394e-01 -7.06111252e-01 -1.25626743e-01 -1.04136789e+00 -3.69546533e-01 -8.54696929e-01 -3.20480131e-02 -1.63181603e-01 3.23041886e-01 -6.06137700e-02 -1.42339140e-01 6.67786241e-01 -1.70206881e+00 1.96012750e-01 7.45280206e-01 1.20939064e+00 2.83713192e-01 2.78278112e-01 1.40328771e-02 6.04342103e-01 -5.13588071e-01 -2.27131516e-01 3.05850446e-01 -8.41951147e-02 -1.83460757e-01 -2.14179263e-01 2.99748957e-01 -4.62861627e-01 -5.91402948e-01 8.78936946e-01 5.57050645e-01 1.44127775e-02 3.63852054e-01 -1.02592969e+00 6.64898083e-02 7.93021917e-01 -7.47668207e-01 6.01753294e-01 -2.56365061e-01 6.20351508e-02 6.54595792e-01 -6.03447735e-01 1.06193855e-01 8.25083733e-01 6.98314905e-01 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-5.55626094e-01 -5.39899290e-01 -8.71093333e-01 -4.64373976e-01 -6.83894008e-02 -7.78611541e-01 5.26230149e-02 -4.66429561e-01]	[8.32797908782959, 2.852698564529419]
d6fcaaea-307e-4c09-b057-5a95cdd9ffd0	handseg-an-automatically-labeled-dataset-for	1711.05944	null	http://arxiv.org/abs/1711.05944v4	http://arxiv.org/pdf/1711.05944v4.pdf	HandSeg: An Automatically Labeled Dataset for Hand Segmentation from Depth Images	We propose an automatic method for generating high-quality annotations for depth-based hand segmentation, and introduce a large-scale hand segmentation dataset. Existing datasets are typically limited to a single hand. By exploiting the visual cues given by an RGBD sensor and a pair of colored gloves, we automatically generate dense annotations for two hand segmentation. This lowers the cost/complexity of creating high quality datasets, and makes it easy to expand the dataset in the future. We further show that existing datasets, even with data augmentation, are not sufficient to train a hand segmentation algorithm that can distinguish two hands. Source and datasets will be made publicly available.	['Sri Raghu Malireddi', 'Andrea Tagliasacchi', 'Vincent Lepetit', 'Kwang Moo Yi', 'Franziska Mueller', 'Christian Theobalt', 'Abhishake Kumar Bojja', 'Markus Oberweger']	2017-11-16	null	null	null	null	['hand-segmentation']	['computer-vision']	[ 2.09061667e-01 7.17273429e-02 -1.94352403e-01 -3.05076122e-01 -6.62361920e-01 -1.06067550e+00 1.13159351e-01 -2.93637246e-01 -2.75647908e-01 5.97416818e-01 4.56217974e-02 -2.01267332e-01 2.75577515e-01 -6.52870595e-01 -4.28546041e-01 -3.88728648e-01 4.15358931e-01 7.69384027e-01 6.37123644e-01 6.25100732e-02 -2.90260725e-02 7.41740644e-01 -1.57790446e+00 4.66848351e-02 5.86551130e-01 8.15004051e-01 4.12231982e-01 1.06719220e+00 -7.91625381e-02 3.69166315e-01 -5.88180542e-01 -4.21519250e-01 6.98072910e-01 -5.27260363e-01 -1.32533479e+00 4.55467373e-01 5.29798031e-01 -8.69686127e-01 -3.87879908e-01 6.70781970e-01 9.25495982e-01 -3.40953730e-02 3.66972208e-01 -1.33778834e+00 -4.54569459e-01 3.85462314e-01 -5.06104708e-01 -1.17989220e-01 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61a92636-85c9-4466-93cc-47fb449ffe9d	learning-by-aligning-2d-skeleton-sequences-in	2305.19480	null	https://arxiv.org/abs/2305.19480v2	https://arxiv.org/pdf/2305.19480v2.pdf	Learning by Aligning 2D Skeleton Sequences in Time	This paper presents a novel self-supervised temporal video alignment framework which is useful for several fine-grained human activity understanding applications. In contrast with the state-of-the-art method of CASA, where sequences of 3D skeleton coordinates are taken directly as input, our key idea is to use sequences of 2D skeleton heatmaps as input. Unlike CASA which performs self-attention in the temporal domain only, we feed 2D skeleton heatmaps to a video transformer which performs self-attention both in the spatial and temporal domains for extracting effective spatiotemporal and contextual features. In addition, we introduce simple heatmap augmentation techniques based on 2D skeletons for self-supervised learning. Despite the lack of 3D information, our approach achieves not only higher accuracy but also better robustness against missing and noisy keypoints than CASA. Furthermore, extensive evaluations on three public datasets, i.e., Penn Action, IKEA ASM, and H2O, demonstrate that our approach outperforms previous methods in different fine-grained human activity understanding tasks. Finally, fusing 2D skeleton heatmaps with RGB videos yields the state-of-the-art on all metrics and datasets. To the best of our knowledge, our work is the first to utilize 2D skeleton heatmap inputs and the first to explore multi-modality fusion for temporal video alignment.	['M. Zeeshan Zia', 'Andrey Konin', 'Murad Popattia', 'M. Hassan Ahmed', 'Ahmed Mehmood', 'Muhammad Ahmed', 'Quoc-Huy Tran']	2023-05-31	null	null	null	null	['video-alignment']	['computer-vision']	[ 3.80930752e-01 -2.19832599e-01 -3.70850891e-01 -2.03847975e-01 -6.82049155e-01 -3.43851268e-01 7.24738359e-01 -3.02118585e-02 -5.41669965e-01 3.80115569e-01 4.26188111e-01 8.93470645e-02 -1.22252978e-01 -6.82981551e-01 -8.66694570e-01 -4.75872874e-01 -1.16728634e-01 2.66088784e-01 4.29211497e-01 -3.13694268e-01 1.40530661e-01 3.53085458e-01 -1.69154751e+00 2.53384709e-01 5.59444070e-01 1.20848858e+00 -1.47439808e-01 5.93835056e-01 -2.04341523e-02 8.35793376e-01 -2.57627398e-01 -2.16905668e-01 4.24890637e-01 -5.38038850e-01 -1.00393105e+00 2.35874549e-01 7.06942618e-01 -4.17883247e-01 -5.25937855e-01 6.77256227e-01 3.54693651e-01 3.69010687e-01 3.49779963e-01 -1.43197548e+00 -3.17760110e-01 1.29875436e-01 -8.26724172e-01 3.03241789e-01 9.68307316e-01 3.53613287e-01 8.62957358e-01 -7.36573458e-01 7.00095296e-01 1.10120881e+00 7.40428030e-01 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-2.00467318e-01 9.06708658e-01 4.60511893e-01 -1.06091952e+00 -1.92264244e-02 3.70338172e-01]	[7.950120449066162, 0.4408995509147644]
1a53c36e-1577-45df-aea8-8462c5335c98	thompson-sampling-for-combinatorial-semi-2	2005.06725	null	https://arxiv.org/abs/2005.06725v1	https://arxiv.org/pdf/2005.06725v1.pdf	Thompson Sampling for Combinatorial Semi-bandits with Sleeping Arms and Long-Term Fairness Constraints	We study the combinatorial sleeping multi-armed semi-bandit problem with long-term fairness constraints~(CSMAB-F). To address the problem, we adopt Thompson Sampling~(TS) to maximize the total rewards and use virtual queue techniques to handle the fairness constraints, and design an algorithm called \emph{TS with beta priors and Bernoulli likelihoods for CSMAB-F~(TSCSF-B)}. Further, we prove TSCSF-B can satisfy the fairness constraints, and the time-averaged regret is upper bounded by $\frac{N}{2\eta} + O\left(\frac{\sqrt{mNT\ln T}}{T}\right)$, where $N$ is the total number of arms, $m$ is the maximum number of arms that can be pulled simultaneously in each round~(the cardinality constraint) and $\eta$ is the parameter trading off fairness for rewards. By relaxing the fairness constraints (i.e., let $\eta \rightarrow \infty$), the bound boils down to the first problem-independent bound of TS algorithms for combinatorial sleeping multi-armed semi-bandit problems. Finally, we perform numerical experiments and use a high-rating movie recommendation application to show the effectiveness and efficiency of the proposed algorithm.	['QiPeng Wang', 'Yifan Xu', 'Bingshan Hu', 'Zhiming Huang', 'Jianping Pan']	2020-05-14	null	null	null	null	['movie-recommendation']	['miscellaneous']	[ 1.73415914e-02 -5.59541993e-02 -5.42206943e-01 -3.71507585e-01 -9.19930696e-01 -6.62254870e-01 -3.49745750e-01 -3.34699482e-01 -8.46328378e-01 1.23205948e+00 -4.04488295e-01 -7.98564672e-01 -8.54282737e-01 -7.53988862e-01 -6.33561075e-01 -8.95643711e-01 -1.91471264e-01 7.46189475e-01 -7.94368535e-02 -1.74855188e-01 2.82982737e-01 4.59139556e-01 -9.54052687e-01 -2.03605905e-01 1.00887799e+00 1.42996156e+00 1.98270530e-02 6.58872783e-01 -2.84150422e-01 8.40173542e-01 -5.80678046e-01 -6.87573195e-01 4.73875344e-01 -5.17708004e-01 -8.30668807e-01 -1.69606626e-01 -2.08615229e-01 -5.58958054e-01 -4.60991651e-01 1.02585506e+00 3.83000642e-01 5.13069987e-01 3.35363626e-01 -1.39188123e+00 2.45394111e-02 1.04127145e+00 -1.36614966e+00 6.63190722e-01 -2.19203681e-02 -2.08205640e-01 1.05253518e+00 -9.31336805e-02 6.07789084e-02 1.31212437e+00 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a957fe64-e92f-4e6f-9cee-ee234fbb72ab	avatarclip-zero-shot-text-driven-generation	2205.08535	null	https://arxiv.org/abs/2205.08535v1	https://arxiv.org/pdf/2205.08535v1.pdf	AvatarCLIP: Zero-Shot Text-Driven Generation and Animation of 3D Avatars	3D avatar creation plays a crucial role in the digital age. However, the whole production process is prohibitively time-consuming and labor-intensive. To democratize this technology to a larger audience, we propose AvatarCLIP, a zero-shot text-driven framework for 3D avatar generation and animation. Unlike professional software that requires expert knowledge, AvatarCLIP empowers layman users to customize a 3D avatar with the desired shape and texture, and drive the avatar with the described motions using solely natural languages. Our key insight is to take advantage of the powerful vision-language model CLIP for supervising neural human generation, in terms of 3D geometry, texture and animation. Specifically, driven by natural language descriptions, we initialize 3D human geometry generation with a shape VAE network. Based on the generated 3D human shapes, a volume rendering model is utilized to further facilitate geometry sculpting and texture generation. Moreover, by leveraging the priors learned in the motion VAE, a CLIP-guided reference-based motion synthesis method is proposed for the animation of the generated 3D avatar. Extensive qualitative and quantitative experiments validate the effectiveness and generalizability of AvatarCLIP on a wide range of avatars. Remarkably, AvatarCLIP can generate unseen 3D avatars with novel animations, achieving superior zero-shot capability.	['Ziwei Liu', 'Lei Yang', 'Zhongang Cai', 'Liang Pan', 'Mingyuan Zhang', 'Fangzhou Hong']	2022-05-17	null	null	null	null	['texture-synthesis']	['computer-vision']	[-1.06867269e-01 1.95246786e-01 2.59190142e-01 1.05807610e-01 -3.41446370e-01 -8.50192130e-01 7.97489524e-01 -6.29765272e-01 1.37845069e-01 1.93032280e-01 2.17162326e-01 -1.59054205e-01 5.36878288e-01 -8.93389344e-01 -6.50401294e-01 -5.42295635e-01 3.43984008e-01 6.08155251e-01 -6.34400696e-02 -6.13998592e-01 7.21756220e-02 6.66316926e-01 -1.33952379e+00 -9.85536650e-02 8.04958165e-01 8.41948211e-01 1.91293523e-01 9.40568686e-01 -1.20493367e-01 6.00235760e-01 -5.88125765e-01 -4.07417893e-01 4.18354779e-01 -6.85083628e-01 -4.44875807e-01 4.46095079e-01 3.15623462e-01 -7.07854211e-01 -4.47344512e-01 8.00024390e-01 5.71597099e-01 3.64344716e-01 7.17013061e-01 -1.32626712e+00 -8.08293939e-01 3.98882687e-01 -7.02324033e-01 -5.12723625e-01 8.11012268e-01 8.91891181e-01 7.71584928e-01 -9.70418274e-01 8.83339584e-01 1.50180495e+00 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b15f86a0-e3f6-43f8-897e-f48494b658bf	agents-that-listen-high-throughput	2107.02195	null	https://arxiv.org/abs/2107.02195v1	https://arxiv.org/pdf/2107.02195v1.pdf	Agents that Listen: High-Throughput Reinforcement Learning with Multiple Sensory Systems	Humans and other intelligent animals evolved highly sophisticated perception systems that combine multiple sensory modalities. On the other hand, state-of-the-art artificial agents rely mostly on visual inputs or structured low-dimensional observations provided by instrumented environments. Learning to act based on combined visual and auditory inputs is still a new topic of research that has not been explored beyond simple scenarios. To facilitate progress in this area we introduce a new version of VizDoom simulator to create a highly efficient learning environment that provides raw audio observations. We study the performance of different model architectures in a series of tasks that require the agent to recognize sounds and execute instructions given in natural language. Finally, we train our agent to play the full game of Doom and find that it can consistently defeat a traditional vision-based adversary. We are currently in the process of merging the augmented simulator with the main ViZDoom code repository. Video demonstrations and experiment code can be found at https://sites.google.com/view/sound-rl.	['Aleksei Petrenko', 'Anssi Kanervisto', 'Shashank Hegde']	2021-07-05	null	null	null	null	['game-of-doom']	['playing-games']	[-3.89978811e-02 -9.83988792e-02 4.13071781e-01 -1.21497102e-01 -4.64649945e-01 -7.84749925e-01 7.02365279e-01 -1.75476484e-02 -8.74900043e-01 5.46715021e-01 -1.23116754e-01 -2.88886815e-01 2.54819810e-01 -4.97850806e-01 -4.97830451e-01 -5.58749080e-01 -1.60829529e-01 6.23501122e-01 5.25414824e-01 -3.82857770e-01 3.26685965e-01 3.42651308e-01 -1.94245565e+00 1.27829358e-01 2.07171708e-01 5.25629401e-01 4.29628313e-01 1.39443707e+00 5.15605092e-01 7.36539364e-01 -8.30205083e-01 4.63088751e-02 3.26697499e-01 -5.21406174e-01 -4.88440096e-01 -3.42297763e-01 3.06460917e-01 -3.89549106e-01 -2.02461973e-01 9.45833802e-01 9.21927214e-01 2.90794164e-01 4.45184976e-01 -1.45383263e+00 -4.66718376e-01 3.46185654e-01 -5.40824570e-02 3.10907334e-01 6.93611264e-01 8.28632712e-01 8.42876792e-01 -5.72202325e-01 3.41226518e-01 1.28682804e+00 4.15802360e-01 7.69366264e-01 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b549c1cd-ffc0-4d09-b5b5-b6fd702e7fce	stn-homography-estimate-homography-parameters	1906.02539	null	https://arxiv.org/abs/1906.02539v1	https://arxiv.org/pdf/1906.02539v1.pdf	STN-Homography: estimate homography parameters directly	In this paper, we introduce the STN-Homography model to directly estimate the homography matrix between image pair. Different most CNN-based homography estimation methods which use an alternative 4-point homography parameterization, we use prove that, after coordinate normalization, the variance of elements of coordinate normalized $3\times3$ homography matrix is very small and suitable to be regressed well with CNN. Based on proposed STN-Homography, we use a hierarchical architecture which stacks several STN-Homography models and successively reduce the estimation error. Effectiveness of the proposed method is shown through experiments on MSCOCO dataset, in which it significantly outperforms the state-of-the-art. The average processing time of our hierarchical STN-Homography with 1 stage is only 4.87 ms on the GPU, and the processing time for hierarchical STN-Homography with 3 stages is 17.85 ms. The code will soon be open sourced.	['Qiang Zhou', 'Xin Li']	2019-06-06	null	null	null	null	['homography-estimation']	['computer-vision']	[-2.00287059e-01 -4.19155173e-02 1.54997855e-01 -2.99253035e-02 -3.11068773e-01 -2.65347987e-01 3.82019341e-01 -3.84999156e-01 -3.42910409e-01 3.30017716e-01 -4.08039689e-02 -2.04325780e-01 2.72941202e-01 -9.31103110e-01 -1.06881177e+00 -5.22253990e-01 1.92498595e-01 3.74913007e-01 2.65289128e-01 -2.24997163e-01 5.88708103e-01 4.71464425e-01 -1.08170938e+00 -2.70658225e-01 6.02344155e-01 1.04154956e+00 -2.49461625e-02 8.30224991e-01 3.51487249e-01 3.35066050e-01 -3.78216654e-01 -5.59200644e-01 7.86888599e-01 -5.54939687e-01 -4.90053356e-01 7.17385039e-02 9.23157692e-01 -6.57102346e-01 -7.11223483e-01 1.25588572e+00 4.31907088e-01 3.38148661e-02 2.90988833e-01 -1.04057717e+00 -3.57611805e-01 2.80726284e-01 -7.22784698e-01 -9.80789661e-02 1.02514260e-01 -5.71391732e-02 7.98311174e-01 -7.15951085e-01 9.47143793e-01 1.21749210e+00 7.91419804e-01 -2.19520200e-02 -1.05036473e+00 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9.62007567e-02 1.66169375e-01 -5.17952085e-01 -5.12768924e-01 -4.92045470e-03]	[8.586602210998535, -2.2151410579681396]
366bd791-618d-494f-8cfb-070a4c5f75cf	multimodal-multipart-learning-for-action	1507.08761	null	http://arxiv.org/abs/1507.08761v1	http://arxiv.org/pdf/1507.08761v1.pdf	Multimodal Multipart Learning for Action Recognition in Depth Videos	The articulated and complex nature of human actions makes the task of action recognition difficult. One approach to handle this complexity is dividing it to the kinetics of body parts and analyzing the actions based on these partial descriptors. We propose a joint sparse regression based learning method which utilizes the structured sparsity to model each action as a combination of multimodal features from a sparse set of body parts. To represent dynamics and appearance of parts, we employ a heterogeneous set of depth and skeleton based features. The proper structure of multimodal multipart features are formulated into the learning framework via the proposed hierarchical mixed norm, to regularize the structured features of each part and to apply sparsity between them, in favor of a group feature selection. Our experimental results expose the effectiveness of the proposed learning method in which it outperforms other methods in all three tested datasets while saturating one of them by achieving perfect accuracy.	['Tian-Tsong Ng', 'Gang Wang', 'Amir Shahroudy', 'Qingxiong Yang']	2015-07-31	null	null	null	null	['multimodal-activity-recognition']	['computer-vision']	[ 3.30860436e-01 -1.16352409e-01 -4.37159270e-01 -1.55359030e-01 -5.04915893e-01 -2.07372010e-01 7.26554096e-01 -1.29799575e-01 -1.85607970e-01 6.74071014e-01 7.02373505e-01 7.16835439e-01 -4.31160778e-01 -3.35173160e-01 -3.76660645e-01 -9.70444441e-01 -6.23337738e-02 5.09700954e-01 2.48006791e-01 -2.24964842e-01 2.07013234e-01 4.70277101e-01 -1.86176169e+00 4.52707201e-01 7.48401344e-01 1.08249867e+00 -1.03510588e-01 3.39806914e-01 -2.54510101e-02 1.05243814e+00 -1.96005955e-01 -1.47374496e-01 5.49348950e-01 -4.92734820e-01 -5.49995363e-01 7.81035125e-01 4.78227645e-01 -2.08456233e-01 -2.57174611e-01 7.98401952e-01 3.57984126e-01 3.77757251e-01 8.78720880e-01 -1.11074638e+00 4.98811752e-02 2.60659993e-01 -8.72462273e-01 -2.14994252e-02 8.45706105e-01 -2.50653923e-02 9.94169176e-01 -8.20401788e-01 6.21820986e-01 1.32484937e+00 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5ce598d7-fdf3-4b2c-be2e-67e6a1e468ff	counterfactual-explanations-for-predictive	2202.12018	null	https://arxiv.org/abs/2202.12018v1	https://arxiv.org/pdf/2202.12018v1.pdf	Counterfactual Explanations for Predictive Business Process Monitoring	Predictive business process monitoring increasingly leverages sophisticated prediction models. Although sophisticated models achieve consistently higher prediction accuracy than simple models, one major drawback is their lack of interpretability, which limits their adoption in practice. We thus see growing interest in explainable predictive business process monitoring, which aims to increase the interpretability of prediction models. Existing solutions focus on giving factual explanations.While factual explanations can be helpful, humans typically do not ask why a particular prediction was made, but rather why it was made instead of another prediction, i.e., humans are interested in counterfactual explanations. While research in explainable AI produced several promising techniques to generate counterfactual explanations, directly applying them to predictive process monitoring may deliver unrealistic explanations, because they ignore the underlying process constraints. We propose LORELEY, a counterfactual explanation technique for predictive process monitoring, which extends LORE, a recent explainable AI technique. We impose control flow constraints to the explanation generation process to ensure realistic counterfactual explanations. Moreover, we extend LORE to enable explaining multi-class classification models. Experimental results using a real, public dataset indicate that LORELEY can approximate the prediction models with an average fidelity of 97.69\% and generate realistic counterfactual explanations.	['Klaus Pohl', 'Andreas Metzger', 'Tsung-Hao Huang']	2022-02-24	null	null	null	null	['counterfactual-explanation', 'predictive-process-monitoring']	['miscellaneous', 'time-series']	[ 5.68670094e-01 1.01922774e+00 -5.82780480e-01 -5.15720844e-01 -2.45808829e-02 -2.95872718e-01 1.03231537e+00 1.57060504e-01 3.61840755e-01 9.44677889e-01 4.42749947e-01 -9.16353047e-01 -4.43656474e-01 -8.90699089e-01 -7.00025260e-01 -9.32364985e-02 1.23950183e-01 7.65777349e-01 -3.10230702e-01 2.99043775e-01 5.21339774e-01 4.54313345e-02 -1.53441834e+00 6.16464853e-01 1.11718869e+00 7.48951554e-01 -9.76397991e-02 6.96524799e-01 -2.52305478e-01 1.40980303e+00 -5.12872577e-01 -6.54285133e-01 2.80557603e-01 -6.59023702e-01 -7.92696357e-01 1.52850255e-01 -9.63426009e-02 -2.68491864e-01 2.26242527e-01 6.05267346e-01 -4.02418017e-01 4.04310562e-02 7.97128618e-01 -2.00511003e+00 -9.53048885e-01 1.00557601e+00 -3.71077061e-01 1.06423080e-01 5.36176741e-01 5.88297963e-01 1.28183270e+00 -2.83318222e-01 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fe7ed137-9a8d-4d20-98e8-8fcd95f8deaf	fast-hand-detection-in-collaborative-learning	2110.07070	null	https://arxiv.org/abs/2110.07070v1	https://arxiv.org/pdf/2110.07070v1.pdf	Fast Hand Detection in Collaborative Learning Environments	Long-term object detection requires the integration of frame-based results over several seconds. For non-deformable objects, long-term detection is often addressed using object detection followed by video tracking. Unfortunately, tracking is inapplicable to objects that undergo dramatic changes in appearance from frame to frame. As a related example, we study hand detection over long video recordings in collaborative learning environments. More specifically, we develop long-term hand detection methods that can deal with partial occlusions and dramatic changes in appearance. Our approach integrates object-detection, followed by time projections, clustering, and small region removal to provide effective hand detection over long videos. The hand detector achieved average precision (AP) of 72% at 0.5 intersection over union (IoU). The detection results were improved to 81% by using our optimized approach for data augmentation. The method runs at 4.7x the real-time with AP of 81% at 0.5 intersection over the union. Our method reduced the number of false-positive hand detections by 80% by improving IoU ratios from 0.2 to 0.5. The overall hand detection system runs at 4x real-time.	['Carlos LopezLeiva', 'Sylvia Celedon Pattichis', 'Marios S. Pattichis', 'Venkatesh Jatla', 'Sravani Teeparthi']	2021-10-13	null	null	null	null	['hand-detection']	['computer-vision']	[-3.58706564e-02 -3.95641804e-01 1.66903540e-01 2.25956410e-01 -8.47813547e-01 -7.71307290e-01 1.74690947e-01 2.49733310e-02 -6.73936963e-01 6.49438083e-01 -2.06492618e-01 -1.75320059e-02 1.14948750e-01 -2.79320598e-01 -8.12249780e-01 -6.41059756e-01 -1.83407709e-01 3.22757810e-01 9.37530935e-01 2.70652294e-01 6.39184564e-02 9.67905879e-01 -1.71155918e+00 2.33301297e-01 4.15833354e-01 6.22567654e-01 4.14466202e-01 1.29843831e+00 -2.46067401e-02 5.64507544e-01 -9.57287192e-01 1.36613110e-02 3.37877810e-01 2.81260222e-01 -5.70281208e-01 3.91603053e-01 1.18585873e+00 -8.17420840e-01 -2.44250819e-01 8.88474703e-01 8.41164589e-01 1.83781222e-01 2.27655962e-01 -1.36557424e+00 -1.14935026e-01 2.84080692e-02 -1.04508758e+00 3.92085612e-01 6.03629529e-01 4.42253053e-01 5.36814928e-01 -1.07055354e+00 7.81820774e-01 1.35704136e+00 7.06273854e-01 6.19990468e-01 -1.30667293e+00 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b80ff0a5-3fd2-4fcf-a463-f02bf77a4c9a	incorporating-the-rhetoric-of-scientific	null	null	https://aclanthology.org/2022.sdp-1.7	https://aclanthology.org/2022.sdp-1.7.pdf	Incorporating the Rhetoric of Scientific Language into Sentence Embeddings using Phrase-guided Distant Supervision and Metric Learning	Communicative functions are an important rhetorical feature of scientific writing. Sentence embeddings that contain such features are highly valuable for the argumentative analysis of scientific documents, with applications in document alignment, recommendation, and academic writing assistance. Moreover, embeddings can provide a possible solution to the open-set problem, where models need to generalize to new communicative functions unseen at training time. However, existing sentence representation models are not suited for detecting functional similarity since they only consider lexical or semantic similarities. To remedy this, we propose a combined approach of distant supervision and metric learning to make a representation model more aware of the functional part of a sentence. We first leverage an existing academic phrase database to label sentences automatically with their functions. Then, we train an embedding model to capture similarities and dissimilarities from a rhetorical perspective. The experimental results demonstrate that the embeddings obtained from our model are more advantageous than existing models when retrieving functionally similar sentences. We also provide an extensive analysis of the performance differences between five metric learning objectives, revealing that traditional methods (e.g., softmax cross-entropy loss and triplet loss) outperform state-of-the-art techniques.	['Akiko Aizawa', 'Kaito Sugimoto']	null	null	null	null	sdp-coling-2022-10	['sentence-embeddings', 'sentence-embeddings']	['methodology', 'natural-language-processing']	[ 7.83272013e-02 7.27319270e-02 -3.84925008e-01 -4.33920622e-01 -5.74041843e-01 -5.52378595e-01 6.65060163e-01 7.40918517e-01 -5.06105363e-01 6.47949755e-01 5.69413364e-01 -3.96716505e-01 -3.28231424e-01 -6.92175508e-01 -4.46818560e-01 -4.89128798e-01 3.93246889e-01 3.78285021e-01 -2.59046882e-01 -5.01320422e-01 8.07717979e-01 2.39848793e-01 -1.42612505e+00 2.52629191e-01 1.24430501e+00 7.44048297e-01 3.75967175e-01 2.61769921e-01 -6.43105268e-01 7.21358895e-01 -8.49440813e-01 -5.51139116e-01 -1.51465863e-01 -3.49454254e-01 -1.07317841e+00 -3.50433707e-01 3.33346426e-01 1.13572590e-01 -1.62761346e-01 1.01508701e+00 6.92680776e-01 2.21807301e-01 8.01848054e-01 -8.06062400e-01 -1.28685391e+00 6.71346307e-01 -3.59772652e-01 4.55316216e-01 5.56341112e-01 -3.84392202e-01 1.57802045e+00 -1.04803991e+00 6.57861352e-01 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bd9655b8-2221-413d-9563-f32aac645869	eica-at-semeval-2017-task-4-a-simple	null	null	https://aclanthology.org/S17-2124	https://aclanthology.org/S17-2124.pdf	EICA at SemEval-2017 Task 4: A Simple Convolutional Neural Network for Topic-based Sentiment Classification	This paper describes our approach for SemEval-2017 Task 4 - Sentiment Analysis in Twitter (SAT). Its five subtasks are divided into two categories: (1) sentiment classification, i.e., predicting topic-based tweet sentiment polarity, and (2) sentiment quantification, that is, estimating the sentiment distributions of a set of given tweets. We build a convolutional sentence classification system for the task of SAT. Official results show that the experimental results of our system are comparative.	['Yufei Xie', 'Shiyun Chen', 'Maoquan Wang', 'Lu Zhao']	2017-08-01	null	null	null	semeval-2017-8	['twitter-sentiment-analysis']	['natural-language-processing']	[-1.28870383e-01 -1.32760525e-01 -3.40650916e-01 -1.10083926e+00 -7.18573570e-01 -4.60919559e-01 6.46720529e-01 2.50220865e-01 -4.07731771e-01 5.05738497e-01 6.35467291e-01 -3.31034303e-01 7.64062583e-01 -6.93219602e-01 -4.66342807e-01 -4.18535829e-01 1.58647537e-01 3.67804676e-01 -3.81874926e-02 -6.51666641e-01 3.77776265e-01 -2.35531688e-01 -1.28282166e+00 7.18703330e-01 4.71828163e-01 1.70830548e+00 -5.74774802e-01 7.49913037e-01 -3.64335924e-01 1.36899710e+00 -8.72551799e-01 -7.90430069e-01 -4.35607910e-01 -3.87900732e-02 -1.08177805e+00 -8.19572434e-02 2.00673774e-01 4.40171361e-02 2.61150450e-01 1.08771694e+00 1.59504429e-01 -1.10378057e-01 6.81358337e-01 -1.18267763e+00 -5.26337504e-01 1.01467037e+00 -5.89962542e-01 3.05882514e-01 2.80333430e-01 -3.74690741e-01 1.33826685e+00 -1.03051269e+00 3.41642022e-01 1.03342605e+00 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-6.35168314e-01 -4.06317264e-01 -1.14070427e+00 -6.63994968e-01 4.05936658e-01 5.09277880e-01 -9.13756639e-02 -6.83756888e-01 1.48011744e-02]	[11.183066368103027, 6.8962578773498535]
2e359dfd-14cf-47ca-a71d-b07f646249b1	faster-person-re-identification	2008.06826	null	https://arxiv.org/abs/2008.06826v1	https://arxiv.org/pdf/2008.06826v1.pdf	Faster Person Re-Identification	Fast person re-identification (ReID) aims to search person images quickly and accurately. The main idea of recent fast ReID methods is the hashing algorithm, which learns compact binary codes and performs fast Hamming distance and counting sort. However, a very long code is needed for high accuracy (e.g. 2048), which compromises search speed. In this work, we introduce a new solution for fast ReID by formulating a novel Coarse-to-Fine (CtF) hashing code search strategy, which complementarily uses short and long codes, achieving both faster speed and better accuracy. It uses shorter codes to coarsely rank broad matching similarities and longer codes to refine only a few top candidates for more accurate instance ReID. Specifically, we design an All-in-One (AiO) framework together with a Distance Threshold Optimization (DTO) algorithm. In AiO, we simultaneously learn and enhance multiple codes of different lengths in a single model. It learns multiple codes in a pyramid structure, and encourage shorter codes to mimic longer codes by self-distillation. DTO solves a complex threshold search problem by a simple optimization process, and the balance between accuracy and speed is easily controlled by a single parameter. It formulates the optimization target as a $F_{\beta}$ score that can be optimised by Gaussian cumulative distribution functions. Experimental results on 2 datasets show that our proposed method (CtF) is not only 8% more accurate but also 5x faster than contemporary hashing ReID methods. Compared with non-hashing ReID methods, CtF is $50\times$ faster with comparable accuracy. Code is available at https://github.com/wangguanan/light-reid.	['Zeng-Guang Hou', "Guan'an Wang", 'Shaogang Gong', 'Jian Cheng']	2020-08-16	null	https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/566_ECCV_2020_paper.php	https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123530273.pdf	eccv-2020-8	['code-search', 'code-search', '2048']	['computer-code', 'computer-vision', 'playing-games']	[-2.76119351e-01 -5.14926434e-01 -1.43638089e-01 -4.15911615e-01 -7.63717413e-01 -5.29914320e-01 3.08182001e-01 4.39979523e-01 -7.83390522e-01 5.03719032e-01 3.18088919e-01 1.30787149e-01 -1.27458379e-01 -9.48639274e-01 -5.87571323e-01 -7.44241059e-01 -2.29028583e-01 5.95267415e-01 2.44828254e-01 -1.47229508e-01 3.09504300e-01 2.19411179e-01 -1.65097713e+00 -1.39744073e-01 1.07936049e+00 6.44782722e-01 1.63420796e-01 4.53692049e-01 9.17009637e-02 3.39450091e-01 -3.95290613e-01 -9.05324817e-01 4.77423698e-01 -2.16072738e-01 -6.80294693e-01 -6.60765529e-01 5.46646655e-01 -3.92516136e-01 -7.05540895e-01 1.17591226e+00 8.21387947e-01 6.06237128e-02 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a99da683-b8f4-423b-8629-38772f111a50	exploiting-bilateral-symmetry-in-brain-lesion	1907.08196	null	https://arxiv.org/abs/1907.08196v1	https://arxiv.org/pdf/1907.08196v1.pdf	Exploiting bilateral symmetry in brain lesion segmentation	Brain lesions, including stroke and tumours, have a high degree of variability in terms of location, size, intensity and form, making automatic segmentation difficult. We propose an improvement to existing segmentation methods by exploiting the bilateral quasi-symmetry of healthy brains, which breaks down when lesions are present. Specifically, we use nonlinear registration of a neuroimage to a reflected version of itself ("reflective registration") to determine for each voxel its homologous (corresponding) voxel in the other hemisphere. A patch around the homologous voxel is added as a set of new features to the segmentation algorithm. To evaluate this method, we implemented two different CNN-based multimodal MRI stroke lesion segmentation algorithms, and then augmented them by adding extra symmetry features using the reflective registration method described above. For each architecture, we compared the performance with and without symmetry augmentation, on the SISS Training dataset of the Ischemic Stroke Lesion Segmentation Challenge (ISLES) 2015 challenge. Using affine reflective registration improves performance over baseline, but nonlinear reflective registration gives significantly better results: an improvement in Dice coefficient of 13 percentage points over baseline for one architecture and 9 points for the other. We argue for the broad applicability of adding symmetric features to existing segmentation algorithms, specifically using nonlinear, template-free methods.	['Tanya Schmah', 'Uladzimir Yahorau', 'Kevin Raina']	2019-07-18	null	null	null	null	['ischemic-stroke-lesion-segmentation']	['medical']	[ 4.46333319e-01 1.25115365e-01 4.14693505e-02 -4.92518932e-01 -8.74715388e-01 -7.42589355e-01 8.37609649e-01 2.12291047e-01 -7.86356628e-01 4.45251614e-01 6.28542364e-01 -1.31133333e-01 -1.07697077e-01 -5.86362839e-01 -4.60270494e-01 -8.04348350e-01 -2.13966921e-01 7.30095565e-01 5.67336380e-01 -1.47386909e-01 2.08412603e-01 9.04058695e-01 -9.53467190e-01 4.18741256e-01 6.42629743e-01 6.34422898e-01 -3.16116065e-02 3.54809701e-01 1.40903339e-01 1.73567489e-01 -3.06218863e-01 -2.30746970e-01 5.62330484e-01 -4.06506240e-01 -1.21114528e+00 1.36488425e-02 6.69392407e-01 -3.20818305e-01 -3.67665499e-01 7.11513042e-01 5.43293357e-01 8.19187164e-02 1.03057873e+00 -8.32239866e-01 -1.43967375e-01 5.54865956e-01 -7.27499127e-01 5.68073332e-01 -5.42618288e-03 1.38612285e-01 5.54654181e-01 -4.88608569e-01 1.02984059e+00 9.36198711e-01 8.97721946e-01 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7a9ed627-f935-4394-8a22-4424b525bd26	dukweb-diachronic-word-representations-from	2107.01076	null	https://arxiv.org/abs/2107.01076v2	https://arxiv.org/pdf/2107.01076v2.pdf	DUKweb: Diachronic word representations from the UK Web Archive corpus	Lexical semantic change (detecting shifts in the meaning and usage of words) is an important task for social and cultural studies as well as for Natural Language Processing applications. Diachronic word embeddings (time-sensitive vector representations of words that preserve their meaning) have become the standard resource for this task. However, given the significant computational resources needed for their generation, very few resources exist that make diachronic word embeddings available to the scientific community. In this paper we present DUKweb, a set of large-scale resources designed for the diachronic analysis of contemporary English. DUKweb was created from the JISC UK Web Domain Dataset (1996-2013), a very large archive which collects resources from the Internet Archive that were hosted on domains ending in `.uk'. DUKweb consists of a series word co-occurrence matrices and two types of word embeddings for each year in the JISC UK Web Domain dataset. We show the reuse potential of DUKweb and its quality standards via a case study on word meaning change detection.	['Barbara McGillivray', 'Mihai Cucuringu', 'Marya Bazzi', 'Pierpaolo Basile', 'Adam Tsakalidis']	2021-07-02	null	null	null	null	['diachronic-word-embeddings']	['natural-language-processing']	[-6.40339404e-02 -3.80859137e-01 -2.66424268e-01 -8.91901031e-02 -4.84185338e-01 -8.84350836e-01 9.21138525e-01 8.07963073e-01 -1.22710371e+00 4.89652395e-01 1.05367863e+00 -1.74329996e-01 -2.54711092e-01 -8.77956033e-01 -1.15172908e-01 -4.70917076e-01 -7.84999579e-02 2.67072558e-01 3.71759593e-01 -5.65783799e-01 4.36215192e-01 1.89423725e-01 -1.66322112e+00 2.00380925e-02 3.33489507e-01 5.97425640e-01 2.35806733e-01 6.24149621e-01 -6.17378712e-01 -1.09845415e-01 -2.82600880e-01 -6.97347105e-01 1.20121613e-01 -1.00789055e-01 -9.74305809e-01 -5.29838860e-01 2.10140660e-01 4.15258467e-01 -4.00194645e-01 1.15501475e+00 7.84363210e-01 3.47987473e-01 5.24717629e-01 -9.37501431e-01 -1.19095552e+00 5.87176800e-01 -1.54675543e-01 9.70840096e-01 3.26546431e-01 -1.60732239e-01 1.60415792e+00 -1.23032451e+00 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95ff1f23-75dc-4ab8-8e22-f2cf822a42a2	meshdepth-disconnected-mesh-based-deep-depth	1905.01312	null	https://arxiv.org/abs/1905.01312v2	https://arxiv.org/pdf/1905.01312v2.pdf	TriDepth: Triangular Patch-based Deep Depth Prediction	We propose a novel and efficient representation for single-view depth estimation using Convolutional Neural Networks (CNNs). Point-cloud is generally used for CNN-based 3D scene reconstruction; however it has some drawbacks: (1) it is redundant as a representation for planar surfaces, and (2) no spatial relationships between points are available (e.g, texture and surface). As a more efficient representation, we introduce a triangular-patch-cloud, which represents the surface of the 3D structure using a set of triangular patches, and propose a CNN framework for its 3D structure estimation. In our framework, we create it by separating all the faces in a 2D mesh, which are determined adaptively from the input image, and estimate depths and normals of all the faces. Using a common RGBD-dataset, we show that our representation has a better or comparable performance than the existing point-cloud-based methods, although it has much less parameters.	['Kiyoharu Aizawa', 'Ken Sakurada', 'Masaya Kaneko']	2019-05-03	null	null	null	null	['3d-scene-reconstruction']	['computer-vision']	[ 8.61441046e-02 2.87300795e-02 1.44940257e-01 -1.73576429e-01 -3.71446401e-01 -3.03342521e-01 2.52572864e-01 -1.76963180e-01 -1.13250203e-01 1.57859355e-01 -1.88932121e-01 -6.90866038e-02 1.70278281e-01 -1.20519829e+00 -9.29130733e-01 -4.96057421e-01 2.70213604e-01 5.17680228e-01 5.55921495e-01 -2.00044751e-01 4.27466869e-01 1.00796866e+00 -1.92624569e+00 2.51236618e-01 6.61654472e-01 1.65451598e+00 1.10773332e-01 3.18325281e-01 -3.19065601e-01 2.50752240e-01 -2.95889765e-01 -2.56453335e-01 3.49605411e-01 4.42900807e-02 -6.45363092e-01 3.10354561e-01 6.91585302e-01 -5.61650813e-01 -2.29170173e-01 8.87326598e-01 2.30711445e-01 -3.23455751e-01 7.51539648e-01 -9.02261794e-01 -1.20957218e-01 -5.62944710e-01 -7.69925773e-01 -6.28999770e-01 4.82930243e-01 -3.49095941e-01 5.36714733e-01 -1.27880168e+00 4.82041657e-01 1.31218803e+00 9.02079046e-01 4.32726502e-01 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fb2a077a-5c43-4405-8631-8f052943f492	co-saliency-detection-for-rgbd-images-based	1710.05172	null	http://arxiv.org/abs/1710.05172v1	http://arxiv.org/pdf/1710.05172v1.pdf	Co-saliency Detection for RGBD Images Based on Multi-constraint Feature Matching and Cross Label Propagation	Co-saliency detection aims at extracting the common salient regions from an image group containing two or more relevant images. It is a newly emerging topic in computer vision community. Different from the most existing co-saliency methods focusing on RGB images, this paper proposes a novel co-saliency detection model for RGBD images, which utilizes the depth information to enhance identification of co-saliency. First, the intra saliency map for each image is generated by the single image saliency model, while the inter saliency map is calculated based on the multi-constraint feature matching, which represents the constraint relationship among multiple images. Then, the optimization scheme, namely Cross Label Propagation (CLP), is used to refine the intra and inter saliency maps in a cross way. Finally, all the original and optimized saliency maps are integrated to generate the final co-saliency result. The proposed method introduces the depth information and multi-constraint feature matching to improve the performance of co-saliency detection. Moreover, the proposed method can effectively exploit any existing single image saliency model to work well in co-saliency scenarios. Experiments on two RGBD co-saliency datasets demonstrate the effectiveness of our proposed model.	['Qingming Huang', 'Huazhu Fu', 'Runmin Cong', 'Xiaochun Cao', 'Jianjun Lei', 'Chunping Hou']	2017-10-14	null	null	null	null	['co-saliency-detection']	['computer-vision']	[ 5.05433202e-01 -1.53068304e-01 -8.20734873e-02 -1.02361232e-01 -5.00595272e-01 6.74022734e-02 2.29021832e-01 3.38854104e-01 -4.05917943e-01 2.86798924e-01 1.10636078e-01 1.60368726e-01 -1.73566222e-01 -6.72008872e-01 -4.64376479e-01 -6.67290807e-01 3.70338172e-01 -3.72508764e-01 1.13806307e+00 -2.05015242e-01 6.55326247e-01 1.12911254e-01 -1.87029362e+00 -1.13623496e-02 1.08093083e+00 1.15374053e+00 9.79170561e-01 1.12547964e-01 -1.64632514e-01 6.41085625e-01 -3.85883778e-01 2.71128297e-01 1.15122765e-01 -3.96825373e-01 -6.46311224e-01 2.47770578e-01 -6.82772622e-02 -3.60079221e-02 2.18673185e-01 1.36081457e+00 5.07746279e-01 1.12892017e-02 5.65721840e-02 -1.43932271e+00 -3.96001399e-01 2.97218084e-01 -9.62107718e-01 4.58665520e-01 3.09113860e-01 -1.17792860e-01 7.22014964e-01 -9.83042061e-01 3.38605195e-01 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f7da1c4c-58d2-4d1f-869a-c5d78e67749e	a-regularized-framework-for-sparse-and	1705.07704	null	http://arxiv.org/abs/1705.07704v3	http://arxiv.org/pdf/1705.07704v3.pdf	A Regularized Framework for Sparse and Structured Neural Attention	Modern neural networks are often augmented with an attention mechanism, which tells the network where to focus within the input. We propose in this paper a new framework for sparse and structured attention, building upon a smoothed max operator. We show that the gradient of this operator defines a mapping from real values to probabilities, suitable as an attention mechanism. Our framework includes softmax and a slight generalization of the recently-proposed sparsemax as special cases. However, we also show how our framework can incorporate modern structured penalties, resulting in more interpretable attention mechanisms, that focus on entire segments or groups of an input. We derive efficient algorithms to compute the forward and backward passes of our attention mechanisms, enabling their use in a neural network trained with backpropagation. To showcase their potential as a drop-in replacement for existing ones, we evaluate our attention mechanisms on three large-scale tasks: textual entailment, machine translation, and sentence summarization. Our attention mechanisms improve interpretability without sacrificing performance; notably, on textual entailment and summarization, we outperform the standard attention mechanisms based on softmax and sparsemax.	['Mathieu Blondel', 'Vlad Niculae']	2017-05-22	a-regularized-framework-for-sparse-and-1	http://papers.nips.cc/paper/6926-a-regularized-framework-for-sparse-and-structured-neural-attention	http://papers.nips.cc/paper/6926-a-regularized-framework-for-sparse-and-structured-neural-attention.pdf	neurips-2017-12	['abstractive-sentence-summarization']	['natural-language-processing']	[ 7.12793350e-01 6.76521480e-01 -2.73515344e-01 -6.38099134e-01 -6.69109941e-01 -4.45772737e-01 6.54370964e-01 2.45435297e-01 -6.67016983e-01 7.55451202e-01 6.31551981e-01 -4.89487469e-01 1.32207617e-01 -5.55580080e-01 -1.09911203e+00 -3.77341390e-01 9.08711553e-02 3.77445042e-01 -1.09077245e-01 -3.15147847e-01 3.44220549e-01 3.83707970e-01 -1.16123545e+00 5.43793142e-01 8.96663487e-01 8.60052943e-01 2.33426660e-01 8.90723825e-01 -1.73994705e-01 1.05308735e+00 -7.25980341e-01 -7.09226549e-01 -5.15241250e-02 -3.47719520e-01 -1.20834279e+00 4.63108532e-02 4.61487591e-01 -3.22926193e-01 -3.08383673e-01 8.37994754e-01 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afffb7d4-c168-41ac-92d8-7f66e9d2d520	nsitnlp4if-2019-propaganda-detection-from	null	null	https://aclanthology.org/D19-5021	https://aclanthology.org/D19-5021.pdf	NSIT@NLP4IF-2019: Propaganda Detection from News Articles using Transfer Learning	In this paper, we describe our approach and system description for NLP4IF 2019 Workshop: Shared Task on Fine-Grained Propaganda Detection. Given a sentence from a news article, the task is to detect whether the sentence contains a propagandistic agenda or not. The main contribution of our work is to evaluate the effectiveness of various transfer learning approaches like ELMo, BERT, and RoBERTa for propaganda detection. We show the use of Document Embeddings on the top of Stacked Embeddings combined with LSTM for identification of propagandistic context in the sentence. We further provide analysis of these models to show the effect of oversampling on the provided dataset. In the final test-set evaluation, our system ranked 21st with F1-score of 0.43 in the SLC Task.	['Anubhav Sadana', 'Kartik Aggarwal']	2019-11-01	null	null	null	ws-2019-11	['propaganda-detection']	['natural-language-processing']	[-7.70180002e-02 1.12728581e-01 -1.55552462e-01 -1.72218695e-01 -1.08832467e+00 -4.62987632e-01 1.24878120e+00 3.96317363e-01 -6.25802994e-01 6.34686470e-01 1.08946478e+00 -6.27492130e-01 1.31030068e-01 -7.61559188e-01 -6.94815576e-01 -5.85078418e-01 -2.02150047e-02 1.56773314e-01 1.33007895e-02 -4.04641896e-01 7.80817211e-01 3.07787806e-01 -5.53001106e-01 1.05718064e+00 5.76123357e-01 5.55149972e-01 -2.09144145e-01 9.71358001e-01 9.57395416e-03 1.60084856e+00 -1.23942792e+00 -3.05467218e-01 -3.70882243e-01 -4.23446238e-01 -1.14336610e+00 -4.63374674e-01 7.24409580e-01 -5.31097054e-01 -3.77191931e-01 9.77860272e-01 6.48889661e-01 -1.91564158e-01 9.45475280e-01 -2.99804181e-01 -8.56480896e-01 1.11594713e+00 -4.87456799e-01 8.86716187e-01 3.27442348e-01 -3.22060436e-01 8.48817170e-01 -1.06679976e+00 9.45033491e-01 1.45580375e+00 6.06955171e-01 6.32746875e-01 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5370bf12-587b-4f7a-bf09-7795ae2b8a46	indexing-ai-risks-with-incidents-issues-and	2211.10384	null	https://arxiv.org/abs/2211.10384v1	https://arxiv.org/pdf/2211.10384v1.pdf	Indexing AI Risks with Incidents, Issues, and Variants	Two years after publicly launching the AI Incident Database (AIID) as a collection of harms or near harms produced by AI in the world, a backlog of "issues" that do not meet its incident ingestion criteria have accumulated in its review queue. Despite not passing the database's current criteria for incidents, these issues advance human understanding of where AI presents the potential for harm. Similar to databases in aviation and computer security, the AIID proposes to adopt a two-tiered system for indexing AI incidents (i.e., a harm or near harm event) and issues (i.e., a risk of a harm event). Further, as some machine learning-based systems will sometimes produce a large number of incidents, the notion of an incident "variant" is introduced. These proposed changes mark the transition of the AIID to a new version in response to lessons learned from editing 2,000+ incident reports and additional reports that fall under the new category of "issue."	['Khoa Lam', 'Kevin Paeth', 'Sean McGregor']	2022-11-18	null	null	null	null	['computer-security']	['miscellaneous']	[ 9.88370627e-02 2.90241361e-01 3.53116659e-03 -2.45786041e-01 -8.17009866e-01 -5.21093845e-01 8.86284232e-01 8.97896469e-01 -4.92954552e-01 7.32644141e-01 8.58569741e-01 -4.63803560e-01 -4.43181455e-01 -9.11677361e-01 -5.00652850e-01 -3.27250808e-02 -1.30727157e-01 5.24394035e-01 7.28888214e-02 -1.81087554e-01 5.28541565e-01 5.97316980e-01 -1.47275734e+00 2.34232888e-01 3.20756406e-01 6.93791747e-01 -5.26900470e-01 1.64332986e-01 -1.41359657e-01 1.09717822e+00 -1.21569324e+00 -4.37247872e-01 5.37254930e-01 -3.42056274e-01 -1.12694478e+00 -3.45540971e-01 2.97477394e-01 -5.48935711e-01 -4.91990685e-01 7.37039387e-01 2.13706344e-01 1.56056523e-01 8.26652467e-01 -1.76900530e+00 -4.52505678e-01 4.01282966e-01 -1.85287014e-01 7.73661554e-01 7.73523033e-01 3.97164047e-01 7.44939923e-01 -5.66597581e-01 9.16328788e-01 1.06099653e+00 7.42819309e-01 2.00879186e-01 -9.16885018e-01 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4.83765393e-01 8.76773223e-02 -8.74057353e-01 -6.12912476e-01 4.55518533e-03]	[8.803709030151367, 9.244643211364746]
ea4ad3f3-cf9e-456c-934c-9a75e79fb08e	repurposing-knowledge-graph-embeddings-for	2208.10328	null	https://arxiv.org/abs/2208.10328v1	https://arxiv.org/pdf/2208.10328v1.pdf	Repurposing Knowledge Graph Embeddings for Triple Representation via Weak Supervision	The majority of knowledge graph embedding techniques treat entities and predicates as separate embedding matrices, using aggregation functions to build a representation of the input triple. However, these aggregations are lossy, i.e. they do not capture the semantics of the original triples, such as information contained in the predicates. To combat these shortcomings, current methods learn triple embeddings from scratch without utilizing entity and predicate embeddings from pre-trained models. In this paper, we design a novel fine-tuning approach for learning triple embeddings by creating weak supervision signals from pre-trained knowledge graph embeddings. We develop a method for automatically sampling triples from a knowledge graph and estimating their pairwise similarities from pre-trained embedding models. These pairwise similarity scores are then fed to a Siamese-like neural architecture to fine-tune triple representations. We evaluate the proposed method on two widely studied knowledge graphs and show consistent improvement over other state-of-the-art triple embedding methods on triple classification and triple clustering tasks.	['Yuan An', 'Alexander Kalinowski']	2022-08-22	null	null	null	null	['triple-classification', 'knowledge-graph-embedding', 'knowledge-graph-embeddings', 'knowledge-graph-embeddings']	['graphs', 'graphs', 'graphs', 'methodology']	[-1.89940825e-01 2.82487631e-01 -7.13050246e-01 -4.05342728e-01 -6.22394383e-01 -4.66984093e-01 6.54829681e-01 6.44160986e-01 -1.96020558e-01 5.56069672e-01 4.85177964e-01 -2.80034561e-02 -2.12816939e-01 -1.12800038e+00 -8.86239171e-01 -4.84489053e-01 2.04073489e-02 8.52372289e-01 1.77081630e-01 -2.83608913e-01 7.45705664e-02 3.20477970e-02 -1.60962474e+00 3.65245283e-01 1.01148367e+00 8.30318868e-01 -3.32034439e-01 3.33618701e-01 -2.52098590e-01 7.04145551e-01 -2.60089278e-01 -6.02371454e-01 1.08015485e-01 -3.87352258e-01 -7.90235996e-01 -1.20087206e-01 5.19181609e-01 7.42093474e-02 -7.07819283e-01 9.05011237e-01 2.60337859e-01 -8.35402533e-02 8.04438233e-01 -1.35234833e+00 -1.61286247e+00 8.53711426e-01 -4.70527351e-01 3.80573468e-03 4.59526867e-01 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48a084b2-d0be-4993-92be-b280d3e254c9	dotat-a-domain-oriented-text-annotation-tool-1	null	null	https://aclanthology.org/2022.acl-demo.1	https://aclanthology.org/2022.acl-demo.1.pdf	DoTAT: A Domain-oriented Text Annotation Tool	We propose DoTAT, a domain-oriented text annotation tool. The tool designs and implements functions heavily in need in domain-oriented information extraction. Firstly, the tool supports a multi-person collaborative process with automatically merging and review, which can greatly improve the annotation accuracy. Secondly, the tool provides annotation of events, nested event and nested entity, which are frequently required in domain-related text structuring tasks. Finally, DoTAT provides visual annotation specification definition, automatic batch annotation and iterative annotation to improve annotation efficiency. Experiments on the ACE2005 dataset show that DoTAT can reduce the event annotation time by 19.7% compared with existing annotation tools. The accuracy without review is 84.09%, 1.35% higher than Brat and 2.59% higher than Webanno. The accuracy of DoTAT even reaches 93.76% with review. The demonstration video can be accessed from https://ecust-nlp-docker.oss-cn-shanghai.aliyuncs.com/dotat_demo.mp4. A live demo website is available at https://github.com/FXLP/MarkTool.	['Yi Wang', 'Wen Du', 'Tingting Cai', 'Ming Liang', 'Tong Ruan', 'Yupian Lin']	null	null	null	null	acl-2022-5	['text-annotation']	['natural-language-processing']	[-4.86569136e-01 2.30433494e-01 -1.33269921e-01 -3.60535592e-01 -8.54750693e-01 -7.15242445e-01 6.25675738e-01 3.63666952e-01 -4.99743909e-01 8.25556576e-01 4.47192371e-01 -7.43457377e-02 7.66527876e-02 -4.91701305e-01 -1.87227711e-01 -1.81973100e-01 3.38838071e-01 7.32444942e-01 3.51003289e-01 4.53259163e-02 7.05170184e-02 -1.41673714e-01 -1.24515224e+00 6.48849607e-01 9.07356620e-01 7.89296329e-01 5.05641997e-01 4.84243572e-01 -5.30193627e-01 8.45147729e-01 -7.98321009e-01 -3.29436183e-01 3.65561731e-02 -3.73410791e-01 -1.07800794e+00 -1.64580941e-01 4.81752418e-02 -1.20091170e-01 -5.20807765e-02 9.22034740e-01 6.03375733e-01 1.18581593e-01 4.05325085e-01 -1.46172965e+00 -3.59327257e-01 1.10289836e+00 -4.39512312e-01 1.40561000e-01 6.93043232e-01 -1.39602154e-01 1.00427103e+00 -1.11924088e+00 1.11085677e+00 9.45114911e-01 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2b4fd209-eedc-4f66-84fa-1664666c91da	restorex-ai-a-contrastive-approach-towards	2204.01719	null	https://arxiv.org/abs/2204.01719v1	https://arxiv.org/pdf/2204.01719v1.pdf	RestoreX-AI: A Contrastive Approach towards Guiding Image Restoration via Explainable AI Systems	Modern applications such as self-driving cars and drones rely heavily upon robust object detection techniques. However, weather corruptions can hinder the object detectability and pose a serious threat to their navigation and reliability. Thus, there is a need for efficient denoising, deraining, and restoration techniques. Generative adversarial networks and transformers have been widely adopted for image restoration. However, the training of these methods is often unstable and time-consuming. Furthermore, when used for object detection (OD), the output images generated by these methods may provide unsatisfactory results despite image clarity. In this work, we propose a contrastive approach towards mitigating this problem, by evaluating images generated by restoration models during and post training. This approach leverages OD scores combined with attention maps for predicting the usefulness of restored images for the OD task. We conduct experiments using two novel use-cases of conditional GANs and two transformer methods that probe the robustness of the proposed approach on multi-weather corruptions in the OD task. Our approach achieves an averaged 178 percent increase in mAP between the input and restored images under adverse weather conditions like dust tornadoes and snowfall. We report unique cases where greater denoising does not improve OD performance and conversely where noisy generated images demonstrate good results. We conclude the need for explainability frameworks to bridge the gap between human and machine perception, especially in the context of robust object detection for autonomous vehicles.	['Ketan Kotecha', 'Rahee Walambe', 'Pushkar Jain', 'Aboli Marathe']	2022-04-03	null	null	null	null	['robust-object-detection']	['computer-vision']	[ 4.36215132e-01 1.63514495e-01 4.38286245e-01 -1.37871012e-01 -7.61479318e-01 -5.94064653e-01 8.51069927e-01 -2.84881353e-01 -4.51993763e-01 7.54340351e-01 -1.68968141e-01 -3.77637208e-01 1.75414562e-01 -8.57223451e-01 -1.00321877e+00 -8.94385934e-01 2.29828984e-01 -2.87376996e-03 2.83177793e-01 -4.98520225e-01 1.76902324e-01 5.62657475e-01 -1.95621908e+00 -5.95749952e-02 1.11080635e+00 1.00458217e+00 1.53452486e-01 7.49502659e-01 3.19340706e-01 7.90084541e-01 -9.27569985e-01 -4.83948886e-01 5.67502975e-01 -2.01180711e-01 -1.18819468e-01 1.68078378e-01 7.44107902e-01 -4.35613632e-01 -3.31349701e-01 1.06726766e+00 5.68747640e-01 1.96687728e-01 6.70201242e-01 -1.56938291e+00 -5.45673847e-01 -1.82383150e-01 -3.71064276e-01 4.30558532e-01 1.59086972e-01 6.98335290e-01 5.35866737e-01 -7.17399716e-01 2.58055300e-01 1.21315622e+00 6.44407749e-01 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746c60b2-5fe1-4509-b47e-8dc527fdcc1a	a-prototypical-semantic-decoupling-method-via	2302.13610	null	https://arxiv.org/abs/2302.13610v2	https://arxiv.org/pdf/2302.13610v2.pdf	A Prototypical Semantic Decoupling Method via Joint Contrastive Learning for Few-Shot Name Entity Recognition	Few-shot named entity recognition (NER) aims at identifying named entities based on only few labeled instances. Most existing prototype-based sequence labeling models tend to memorize entity mentions which would be easily confused by close prototypes. In this paper, we proposed a Prototypical Semantic Decoupling method via joint Contrastive learning (PSDC) for few-shot NER. Specifically, we decouple class-specific prototypes and contextual semantic prototypes by two masking strategies to lead the model to focus on two different semantic information for inference. Besides, we further introduce joint contrastive learning objectives to better integrate two kinds of decoupling information and prevent semantic collapse. Experimental results on two few-shot NER benchmarks demonstrate that PSDC consistently outperforms the previous SOTA methods in terms of overall performance. Extensive analysis further validates the effectiveness and generalization of PSDC.	['Weiran Xu', 'QiXiang Gao', 'Xinyue Cui', 'Keqing He', 'Tingfeng Hui', 'Xuefeng Li', 'Chen Zeng', 'Yuxiang Wu', 'Dayuan Fu', 'Daichi Guo', 'LiWen Wang', 'Zechen Wang', 'Guanting Dong']	2023-02-27	null	null	null	null	['few-shot-ner']	['natural-language-processing']	[-1.59095317e-01 3.16459909e-02 -2.04747766e-01 -4.91137743e-01 -5.88994145e-01 -4.91910577e-01 6.11484349e-01 3.94616187e-01 -7.19572961e-01 7.51765668e-01 2.37176761e-01 -2.80629955e-02 -1.32756401e-02 -7.68719375e-01 -3.13541114e-01 -4.04697150e-01 1.09331645e-01 4.11740154e-01 4.74353939e-01 -2.58111000e-01 2.01900885e-01 2.33331859e-01 -1.35905588e+00 1.49253890e-01 1.08503342e+00 3.65332693e-01 3.66748780e-01 1.56163856e-01 -7.49174595e-01 8.23803306e-01 -6.71995461e-01 -6.82735443e-01 -1.27932698e-01 -3.38408679e-01 -9.88510489e-01 -2.88103819e-01 1.37745500e-01 8.16832185e-02 -4.34368737e-02 1.18647122e+00 6.99510098e-01 7.68884957e-01 4.90295678e-01 -1.09620333e+00 -9.41120505e-01 9.61053967e-01 -3.17658186e-01 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92db60b1-82f9-405d-ad52-b8b738b7e07d	separable-structure-modeling-for-semi	null	null	https://ieeexplore.ieee.org/document/9356697	https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9356697	Separable Structure Modeling for Semi-supervised Video Object Segmentation	In this paper, we propose a separable structure modeling approach for semi-supervised video object segmentation. Unlike most existing methods which preclude the semantically structural information of target objects, our method not only captures pixel-level similarity relationships between the reference and target frames but also reveals the separable structure of the specified objects in target frames. Specifically, we first compute a pixel-wise similarity matrix by using representations of reference and target pixels and then select top-rank reference pixels for target pixel classification. According to the prior knowledge from these top-rank reference pixels, we further appoint the representative target pixels for object structure modeling. Particularly, in the structure modeling branch, we extract the shared and individual features that can well represent the whole object and its components, respectively. Moreover, the proposed method is a fast algorithm without online fine-tuning and any post-processing. We conduct extensive experiments and ablation studies on the DAVIS-16, DAVIS-17, and YouTube-VOS datasets, and experimental results on three widely-used datasets demonstrate that our method achieves superior performance, compared with state-of-the-art semi-supervised video object segmentation approaches in terms of speed and accuracy.	['Jie zhou', 'Jiwen Lu', 'Jiahao Li', 'Wencheng Zhu']	2021-02-18	null	null	null	null	['one-shot-visual-object-segmentation']	['computer-vision']	[ 3.56362402e-01 -2.96784133e-01 -4.95837986e-01 -3.98822606e-01 -6.81364000e-01 -3.61773610e-01 1.39092281e-01 -7.43386149e-02 -3.27363759e-01 3.27263236e-01 -2.26413161e-02 1.82670295e-01 -8.15811679e-02 -5.49778402e-01 -5.27045131e-01 -7.84804046e-01 1.18312322e-01 3.20422351e-01 8.67255569e-01 3.25096726e-01 2.40982473e-01 2.99910188e-01 -1.55128801e+00 2.79607624e-01 9.43581462e-01 1.43282986e+00 4.47821498e-01 2.25360706e-01 -1.45224914e-01 8.38532329e-01 -2.80214429e-01 -2.54563782e-02 2.84832537e-01 -4.00407076e-01 -8.87265086e-01 9.39091444e-01 6.88605368e-01 -3.76341850e-01 -5.09964287e-01 1.26317286e+00 1.43315613e-01 1.41139507e-01 6.48025513e-01 -1.18677807e+00 -2.65099347e-01 4.05218840e-01 -8.98084581e-01 3.72550309e-01 2.13770077e-01 2.31547907e-01 9.69001532e-01 -9.09262657e-01 6.44969940e-01 1.19729578e+00 3.60253066e-01 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d1e0cd9f-0d1e-4ce5-b540-acb2b1f053d0	beyond-supervised-vs-unsupervised-1	2206.08347	null	https://arxiv.org/abs/2206.08347v1	https://arxiv.org/pdf/2206.08347v1.pdf	Beyond Supervised vs. Unsupervised: Representative Benchmarking and Analysis of Image Representation Learning	By leveraging contrastive learning, clustering, and other pretext tasks, unsupervised methods for learning image representations have reached impressive results on standard benchmarks. The result has been a crowded field - many methods with substantially different implementations yield results that seem nearly identical on popular benchmarks, such as linear evaluation on ImageNet. However, a single result does not tell the whole story. In this paper, we compare methods using performance-based benchmarks such as linear evaluation, nearest neighbor classification, and clustering for several different datasets, demonstrating the lack of a clear front-runner within the current state-of-the-art. In contrast to prior work that performs only supervised vs. unsupervised comparison, we compare several different unsupervised methods against each other. To enrich this comparison, we analyze embeddings with measurements such as uniformity, tolerance, and centered kernel alignment (CKA), and propose two new metrics of our own: nearest neighbor graph similarity and linear prediction overlap. We reveal through our analysis that in isolation, single popular methods should not be treated as though they represent the field as a whole, and that future work ought to consider how to leverage the complimentary nature of these methods. We also leverage CKA to provide a framework to robustly quantify augmentation invariance, and provide a reminder that certain types of invariance will be undesirable for downstream tasks.	['Abhinav Shrivastava', 'Matthew Gwilliam']	2022-06-16	beyond-supervised-vs-unsupervised	http://openaccess.thecvf.com//content/CVPR2022/html/Gwilliam_Beyond_Supervised_vs._Unsupervised_Representative_Benchmarking_and_Analysis_of_Image_CVPR_2022_paper.html	http://openaccess.thecvf.com//content/CVPR2022/papers/Gwilliam_Beyond_Supervised_vs._Unsupervised_Representative_Benchmarking_and_Analysis_of_Image_CVPR_2022_paper.pdf	cvpr-2022-1	['graph-similarity']	['graphs']	[ 1.08607978e-01 -2.42028572e-02 -3.80331784e-01 -5.78784227e-01 -5.82305312e-01 -7.91174769e-01 9.50013995e-01 4.95038629e-01 -4.32688028e-01 2.08544105e-01 7.90980399e-01 -2.70982295e-01 -4.32792842e-01 -5.15031815e-01 -4.90219921e-01 -6.49095833e-01 -3.48452479e-01 9.57619324e-02 1.89401075e-01 -2.95921594e-01 3.97671491e-01 4.90971953e-01 -1.61868262e+00 2.20670536e-01 4.42866236e-01 6.91858351e-01 -3.78683150e-01 4.63727146e-01 7.07419291e-02 9.98620689e-01 -3.14094007e-01 -5.18874705e-01 3.55611533e-01 -3.75382453e-01 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65e1126b-dd73-4b1d-baa8-4764da2b483c	unified-modeling-of-multi-talker-overlapped	2305.16263	null	https://arxiv.org/abs/2305.16263v1	https://arxiv.org/pdf/2305.16263v1.pdf	Unified Modeling of Multi-Talker Overlapped Speech Recognition and Diarization with a Sidecar Separator	Multi-talker overlapped speech poses a significant challenge for speech recognition and diarization. Recent research indicated that these two tasks are inter-dependent and complementary, motivating us to explore a unified modeling method to address them in the context of overlapped speech. A recent study proposed a cost-effective method to convert a single-talker automatic speech recognition (ASR) system into a multi-talker one, by inserting a Sidecar separator into the frozen well-trained ASR model. Extending on this, we incorporate a diarization branch into the Sidecar, allowing for unified modeling of both ASR and diarization with a negligible overhead of only 768 parameters. The proposed method yields better ASR results compared to the baseline on LibriMix and LibriSpeechMix datasets. Moreover, without sophisticated customization on the diarization task, our method achieves acceptable diarization results on the two-speaker subset of CALLHOME with only a few adaptation steps.	['Helen Meng', 'Xixin Wu', 'Haibin Wu', 'Mingyu Cui', 'Jiawen Kang', 'Lingwei Meng']	2023-05-25	null	null	null	null	['automatic-speech-recognition']	['speech']	[ 2.48483464e-01 1.20878667e-01 7.43886456e-02 -7.55512834e-01 -1.64946043e+00 -6.52490377e-01 3.95423591e-01 -3.20415884e-01 -4.05276954e-01 8.34199935e-02 3.49025071e-01 -5.54517627e-01 3.27252746e-01 1.56512097e-01 -3.64944756e-01 -8.78589034e-01 3.47847015e-01 3.23867887e-01 6.80591315e-02 -1.86852917e-01 -3.73644382e-01 3.52704704e-01 -1.29368639e+00 3.91930968e-01 7.13010728e-01 9.05076981e-01 3.70850563e-01 1.06929290e+00 -1.51477113e-01 4.74753112e-01 -1.05849731e+00 -3.34369123e-01 2.98456162e-01 -4.84663785e-01 -5.37367940e-01 1.59474388e-01 5.78278482e-01 -2.36773372e-01 -4.91610944e-01 5.46006382e-01 1.17525768e+00 2.39747614e-01 2.18274787e-01 -8.32697272e-01 -1.75031319e-01 1.19774735e+00 -4.05466676e-01 5.27439475e-01 3.25290188e-02 -1.41474873e-01 9.88984346e-01 -8.33038688e-01 -1.23707406e-01 1.30206776e+00 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56cb79b7-b83b-4dac-b216-714f9065706c	multiview-compressive-coding-for-3d	2301.08247	null	https://arxiv.org/abs/2301.08247v1	https://arxiv.org/pdf/2301.08247v1.pdf	Multiview Compressive Coding for 3D Reconstruction	A central goal of visual recognition is to understand objects and scenes from a single image. 2D recognition has witnessed tremendous progress thanks to large-scale learning and general-purpose representations. Comparatively, 3D poses new challenges stemming from occlusions not depicted in the image. Prior works try to overcome these by inferring from multiple views or rely on scarce CAD models and category-specific priors which hinder scaling to novel settings. In this work, we explore single-view 3D reconstruction by learning generalizable representations inspired by advances in self-supervised learning. We introduce a simple framework that operates on 3D points of single objects or whole scenes coupled with category-agnostic large-scale training from diverse RGB-D videos. Our model, Multiview Compressive Coding (MCC), learns to compress the input appearance and geometry to predict the 3D structure by querying a 3D-aware decoder. MCC's generality and efficiency allow it to learn from large-scale and diverse data sources with strong generalization to novel objects imagined by DALL$\cdot$E 2 or captured in-the-wild with an iPhone.	['Georgia Gkioxari', 'Christoph Feichtenhofer', 'Jitendra Malik', 'Justin Johnson', 'Chao-yuan Wu']	2023-01-19	null	http://openaccess.thecvf.com//content/CVPR2023/html/Wu_Multiview_Compressive_Coding_for_3D_Reconstruction_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Wu_Multiview_Compressive_Coding_for_3D_Reconstruction_CVPR_2023_paper.pdf	cvpr-2023-1	['single-view-3d-reconstruction']	['computer-vision']	[ 4.66992944e-01 1.11703672e-01 -1.76866487e-01 -4.84029114e-01 -8.82879496e-01 -7.60946631e-01 5.96516371e-01 -4.49979156e-01 2.35207930e-01 2.86739707e-01 1.46998942e-01 -8.38699937e-03 6.13748934e-03 -5.27900755e-01 -1.02420282e+00 -3.60142410e-01 1.94531903e-01 5.18158793e-01 1.33458987e-01 -1.10341264e-02 2.57330269e-01 9.39926445e-01 -2.03529620e+00 4.52582359e-01 2.18633041e-01 1.23047400e+00 5.60818017e-01 6.78671062e-01 -1.46277741e-01 5.69830477e-01 -7.84788877e-02 -9.82060060e-02 7.76545048e-01 -1.00777939e-01 -3.67670923e-01 8.16949308e-01 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5f195e09-8777-4be4-a49f-39ef59fae576	the-mapillary-vistas-dataset-for-semantic	null	null	http://openaccess.thecvf.com/content_iccv_2017/html/Neuhold_The_Mapillary_Vistas_ICCV_2017_paper.html	http://openaccess.thecvf.com/content_ICCV_2017/papers/Neuhold_The_Mapillary_Vistas_ICCV_2017_paper.pdf	The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes	The Mapillary Vistas Dataset is a novel, large-scale street-level image dataset containing 25,000 high-resolution images annotated into 66 object categories with additional, instance-specific labels for 37 classes. Annotation is performed in a dense and fine-grained style by using polygons for delineating individual objects. Our dataset is 5x larger than the total amount of fine annotations for Cityscapes and contains images from all around the world, captured at various conditions regarding weather, season and daytime. Images come from different imaging devices (mobile phones, tablets, action cameras, professional capturing rigs) and differently experienced photographers. In such a way, our dataset has been designed and compiled to cover diversity, richness of detail and geographic extent. As default benchmark tasks, we define semantic image segmentation and instance-specific image segmentation, aiming to significantly further the development of state-of-the-art methods for visual road-scene understanding.	['Gerhard Neuhold', 'Peter Kontschieder', 'Tobias Ollmann', 'Samuel Rota Bulo']	2017-10-01	null	null	null	iccv-2017-10	['road-scene-understanding']	['computer-vision']	[ 3.88021678e-01 1.77897885e-01 -1.47004709e-01 -4.74245727e-01 -5.71276546e-01 -9.28737819e-01 9.67001975e-01 2.13886902e-01 -3.53289157e-01 6.33585930e-01 2.57357270e-01 -2.98156381e-01 1.78569719e-01 -1.24180734e+00 -6.83601439e-01 -3.23735595e-01 -4.93565612e-02 6.52862012e-01 6.33449495e-01 -2.63480276e-01 2.41201699e-01 7.14893401e-01 -1.96006536e+00 2.89226562e-01 8.86374354e-01 9.39143717e-01 4.82217610e-01 7.56346524e-01 -3.43668878e-01 7.03148782e-01 -3.72903049e-01 -3.27031791e-01 4.24185425e-01 1.94633812e-01 -9.23943102e-01 6.54608428e-01 9.69568491e-01 -1.97437420e-01 -1.35612771e-01 1.01363945e+00 3.33258286e-02 9.83024314e-02 5.56369424e-01 -9.15630758e-01 -5.16959012e-01 2.82625437e-01 -6.76930130e-01 3.18570733e-01 9.48292390e-02 3.40642214e-01 9.12274480e-01 -5.03632307e-01 7.39329755e-01 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5.33857822e-01 -1.35230970e+00 7.03755081e-01 -7.36996293e-01 -6.70299113e-01 3.80803317e-01 3.22414309e-01 -7.74543583e-01 -2.15325668e-01 3.52968544e-01]	[8.574701309204102, -1.5598591566085815]
5b3e4597-cab3-4ebb-80e2-28c8c899d4cf	an-inductive-formalization-of-self	1806.08925	null	http://arxiv.org/abs/1806.08925v1	http://arxiv.org/pdf/1806.08925v1.pdf	An Inductive Formalization of Self Reproduction in Dynamical Hierarchies	Formalizing self reproduction in dynamical hierarchies is one of the important problems in Artificial Life (AL) studies. We study, in this paper, an inductively defined algebraic framework for self reproduction on macroscopic organizational levels under dynamical system setting for simulated AL models and explore some existential results. Starting with defining self reproduction for atomic entities we define self reproduction with possible mutations on higher organizational levels in terms of hierarchical sets and the corresponding inductively defined `meta' - reactions. We introduce constraints to distinguish a collection of entities from genuine cases of emergent organizational structures.	['Janardan Misra']	2018-06-23	null	null	null	null	['artificial-life']	['miscellaneous']	[ 1.16443209e-01 6.93672121e-01 4.23499882e-01 1.81894898e-01 7.25168586e-01 -8.07647586e-01 9.81629252e-01 2.96455652e-01 -1.67072207e-01 1.04547763e+00 3.44126582e-01 1.23167671e-01 -4.55827564e-01 -1.10321081e+00 -4.88281935e-01 -9.66054380e-01 -6.21075094e-01 6.15506887e-01 3.76307160e-01 -8.62883151e-01 -2.79671755e-02 6.03082359e-01 -1.86564076e+00 -3.86234336e-02 1.03217924e+00 1.66599005e-01 -2.79874533e-01 9.35496569e-01 1.65302277e-01 1.13443804e+00 -7.45018542e-01 -6.30848557e-02 2.83837885e-01 -8.88462603e-01 -8.51976514e-01 4.38647121e-01 -2.32849300e-01 6.33245468e-01 -2.15890676e-01 8.80180061e-01 3.71052504e-01 1.12383952e-02 1.06519318e+00 -1.33061171e+00 -1.01712573e+00 9.48587060e-01 -1.06144555e-01 2.42311895e-01 3.75247180e-01 2.30220824e-01 7.68726647e-01 -4.04373050e-01 1.02920258e+00 1.20604813e+00 4.98688012e-01 6.33956790e-01 -1.92418718e+00 2.02810556e-01 -3.48719358e-01 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d46b62e5-77fe-43f4-b074-d18b7e5c987f	investigating-generative-adversarial-networks	1803.10132	null	http://arxiv.org/abs/1803.10132v3	http://arxiv.org/pdf/1803.10132v3.pdf	Investigating Generative Adversarial Networks based Speech Dereverberation for Robust Speech Recognition	We investigate the use of generative adversarial networks (GANs) in speech dereverberation for robust speech recognition. GANs have been recently studied for speech enhancement to remove additive noises, but there still lacks of a work to examine their ability in speech dereverberation and the advantages of using GANs have not been fully established. In this paper, we provide deep investigations in the use of GAN-based dereverberation front-end in ASR. First, we study the effectiveness of different dereverberation networks (the generator in GAN) and find that LSTM leads a significant improvement as compared with feed-forward DNN and CNN in our dataset. Second, further adding residual connections in the deep LSTMs can boost the performance as well. Finally, we find that, for the success of GAN, it is important to update the generator and the discriminator using the same mini-batch data during training. Moreover, using reverberant spectrogram as a condition to discriminator, as suggested in previous studies, may degrade the performance. In summary, our GAN-based dereverberation front-end achieves 14%-19% relative CER reduction as compared to the baseline DNN dereverberation network when tested on a strong multi-condition training acoustic model.	['Ke Wang', 'Fei Xiang', 'Sining Sun', 'Yujun Wang', 'Junbo Zhang', 'Lei Xie']	2018-03-27	null	null	null	null	['robust-speech-recognition', 'speech-dereverberation']	['speech', 'speech']	[ 1.02270819e-01 6.51721656e-02 6.40699506e-01 -4.62081134e-02 -9.87108111e-01 -5.04592717e-01 4.29085106e-01 -6.24614537e-01 -2.82319397e-01 7.36171961e-01 5.34532428e-01 -5.92552841e-01 3.87679577e-01 -4.70520765e-01 -7.54624844e-01 -1.01316655e+00 4.02963549e-01 -3.66156809e-02 -1.47750124e-01 -4.92048591e-01 -4.87328947e-01 6.13921404e-01 -1.13713968e+00 9.73170847e-02 8.83205950e-01 7.61520445e-01 3.12377036e-01 1.16218531e+00 2.84353018e-01 7.23864377e-01 -1.60828221e+00 -3.61258954e-01 3.08320224e-01 -9.92927134e-01 -2.50662714e-01 -2.88549721e-01 4.23195660e-01 -4.82796431e-01 -6.67685807e-01 8.24918926e-01 1.28821218e+00 2.71707207e-01 5.71011901e-01 -7.51289606e-01 -7.10135996e-01 7.66982019e-01 -5.41948453e-02 4.27594274e-01 9.29395407e-02 4.18504119e-01 6.75786912e-01 -7.13170588e-01 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1.20328951e+00 7.82056630e-01 -6.08150780e-01 -5.77787340e-01 -1.65606332e+00 -4.61363077e-01 2.13750392e-01 6.68756545e-01 -2.31238648e-01 -8.35921168e-01 -1.31794894e-02]	[15.067460060119629, 6.044388771057129]
a218b965-8230-4cc5-97df-6bb2a8ff869c	probabilistic-polargmm-unsupervised-cluster	2206.12959	null	https://arxiv.org/abs/2206.12959v1	https://arxiv.org/pdf/2206.12959v1.pdf	Probabilistic PolarGMM: Unsupervised Cluster Learning of Very Noisy Projection Images of Unknown Pose	A crucial step in single particle analysis (SPA) of cryogenic electron microscopy (Cryo-EM), 2D classification and alignment takes a collection of noisy particle images to infer orientations and group similar images together. Averaging these aligned and clustered noisy images produces a set of clean images, ready for further analysis such as 3D reconstruction. Fourier-Bessel steerable principal component analysis (FBsPCA) enables an efficient, adaptable, low-rank rotation operator. We extend the FBsPCA to additionally handle translations. In this extended FBsPCA representation, we use a probabilistic polar-coordinate Gaussian mixture model to learn soft clusters in an unsupervised fashion using an expectation maximization (EM) algorithm. The obtained rotational clusters are thus additionally robust to the presence of pairwise alignment imperfections. Multiple benchmarks from simulated Cryo-EM datasets show probabilistic PolarGMM's improved performance in comparisons with standard single-particle Cryo-EM tools, EMAN2 and RELION, in terms of various clustering metrics and alignment errors.	['Chandrajit L. Bajaj', 'Supawit Chockchowwat']	2022-06-26	null	null	null	null	['cryogenic-electron-microscopy-cryo-em']	['computer-vision']	[ 2.23451421e-01 -4.66346771e-01 5.12266278e-01 -3.26260567e-01 -9.37110245e-01 -7.02918112e-01 8.13003242e-01 1.14341609e-01 -7.92845607e-01 8.32455158e-01 -1.53819963e-01 -1.90857694e-01 -3.82964641e-01 -1.50472865e-01 -6.31588995e-01 -1.41763651e+00 -1.04577079e-01 1.28141642e+00 1.29639283e-01 2.77895957e-01 5.17903507e-01 9.61966991e-01 -1.20822179e+00 1.09396351e-03 3.96032065e-01 4.09369916e-01 8.17466319e-01 7.58661807e-01 3.79564047e-01 3.26087773e-01 -3.60518545e-01 1.99826971e-01 -6.18078783e-02 -1.19784489e-01 -9.34643328e-01 2.56662935e-01 -4.16376479e-02 3.46092075e-01 2.53196150e-01 1.07678294e+00 5.39748311e-01 3.33283693e-01 6.63443863e-01 -6.63136899e-01 -1.75180122e-01 -3.67450230e-02 -3.96470249e-01 2.08079726e-01 3.79485786e-01 1.96322083e-01 6.27272367e-01 -1.10829115e+00 1.29581797e+00 1.18395960e+00 4.14943099e-01 1.41749099e-01 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-6.71426773e-01 7.64851570e-01 1.45974964e-01 -8.98315251e-01 -6.15865588e-01 2.59657532e-01]	[13.295042991638184, -3.065812587738037]
433cdfd9-370e-4923-b812-05edd9d42cea	dds-decoupled-dynamic-scene-graph-generation	2301.07666	null	https://arxiv.org/abs/2301.07666v1	https://arxiv.org/pdf/2301.07666v1.pdf	DDS: Decoupled Dynamic Scene-Graph Generation Network	Scene-graph generation involves creating a structural representation of the relationships between objects in a scene by predicting subject-object-relation triplets from input data. However, existing methods show poor performance in detecting triplets outside of a predefined set, primarily due to their reliance on dependent feature learning. To address this issue we propose DDS -- a decoupled dynamic scene-graph generation network -- that consists of two independent branches that can disentangle extracted features. The key innovation of the current paper is the decoupling of the features representing the relationships from those of the objects, which enables the detection of novel object-relationship combinations. The DDS model is evaluated on three datasets and outperforms previous methods by a significant margin, especially in detecting previously unseen triplets.	['B. S. Manjunath', 'Suya You', 'Satish Kumar', 'Raphael Ruschel', 'A S M Iftekhar']	2023-01-18	null	null	null	null	['scene-graph-generation']	['computer-vision']	[ 3.74599278e-01 -1.15641423e-01 8.92726704e-02 -5.06651461e-01 -3.69418234e-01 -5.72786808e-01 5.99658966e-01 3.13204497e-01 3.09124868e-02 5.37191927e-01 1.32538944e-01 1.13142073e-01 -3.57630879e-01 -6.89214230e-01 -5.16044915e-01 -6.34777367e-01 -1.77961633e-01 4.98931289e-01 5.84317148e-01 -1.56131193e-01 1.80448323e-01 7.48203874e-01 -1.82156372e+00 4.66538668e-01 6.67819500e-01 9.23887372e-01 1.77895814e-01 4.95590419e-01 -2.82853004e-02 7.00933099e-01 -6.02262795e-01 -3.56047630e-01 4.34593111e-01 -5.59825122e-01 -4.51669484e-01 2.77191967e-01 7.85431921e-01 -1.90319017e-01 -4.03190017e-01 7.81547844e-01 1.60437182e-01 7.54743889e-02 6.64700449e-01 -1.70633233e+00 -5.03828585e-01 2.34565631e-01 -5.57939351e-01 3.65099341e-01 5.61198831e-01 7.49438256e-02 1.30973852e+00 -9.27831531e-01 7.98618972e-01 1.32557571e+00 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f48c0b1e-b07d-47ab-a5aa-395c344329df	generative-ode-modeling-with-known-unknowns	2003.10775	null	https://arxiv.org/abs/2003.10775v2	https://arxiv.org/pdf/2003.10775v2.pdf	Generative ODE Modeling with Known Unknowns	In several crucial applications, domain knowledge is encoded by a system of ordinary differential equations (ODE), often stemming from underlying physical and biological processes. A motivating example is intensive care unit patients: the dynamics of vital physiological functions, such as the cardiovascular system with its associated variables (heart rate, cardiac contractility and output and vascular resistance) can be approximately described by a known system of ODEs. Typically, some of the ODE variables are directly observed (heart rate and blood pressure for example) while some are unobserved (cardiac contractility, output and vascular resistance), and in addition many other variables are observed but not modeled by the ODE, for example body temperature. Importantly, the unobserved ODE variables are known-unknowns: We know they exist and their functional dynamics, but cannot measure them directly, nor do we know the function tying them to all observed measurements. As is often the case in medicine, and specifically the cardiovascular system, estimating these known-unknowns is highly valuable and they serve as targets for therapeutic manipulations. Under this scenario we wish to learn the parameters of the ODE generating each observed time-series, and extrapolate the future of the ODE variables and the observations. We address this task with a variational autoencoder incorporating the known ODE function, called GOKU-net for Generative ODE modeling with Known Unknowns. We first validate our method on videos of single and double pendulums with unknown length or mass; we then apply it to a model of the cardiovascular system. We show that modeling the known-unknowns allows us to successfully discover clinically meaningful unobserved system parameters, leads to much better extrapolation, and enables learning using much smaller training sets.	['Neta Ravid', 'Danny Eytan', 'Ori Linial', 'Uri Shalit']	2020-03-24	null	https://openreview.net/forum?id=pmvEzAbl7M	https://openreview.net/pdf?id=pmvEzAbl7M	iclr-workshop-deepdiffeq-2019-12	['known-unknowns']	['miscellaneous']	[ 5.91522008e-02 2.12698743e-01 -3.17217886e-01 1.13496691e-01 -7.94504303e-03 -7.57000029e-01 3.40231806e-01 -6.33178428e-02 -5.44194840e-02 1.36927593e+00 3.77086699e-02 -2.45700508e-01 -2.34718114e-01 -5.06176472e-01 -8.34387302e-01 -1.10403514e+00 -3.37691516e-01 6.89843774e-01 -3.72646004e-01 -1.52944565e-01 -3.07844490e-01 4.88051862e-01 -1.04353702e+00 -3.51363361e-01 6.01987004e-01 7.34532297e-01 -2.32129008e-01 1.15462697e+00 4.09810036e-01 1.00293195e+00 -4.37447071e-01 1.58625364e-01 1.53015614e-01 -7.49144852e-01 -5.17036915e-01 5.85073531e-02 -8.31470191e-02 -6.11808479e-01 -6.76622093e-01 5.57270348e-01 2.04163194e-01 2.79206127e-01 1.02517843e+00 -1.19243574e+00 -4.91540551e-01 2.04560220e-01 -5.37841767e-02 8.78254324e-02 -8.07576627e-02 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632616b7-ac3b-4094-ba5d-517940f99090	dynamic-graph-attention-for-anomaly-detection	2307.03761	null	https://arxiv.org/abs/2307.03761v1	https://arxiv.org/pdf/2307.03761v1.pdf	Dynamic Graph Attention for Anomaly Detection in Heterogeneous Sensor Networks	In the era of digital transformation, systems monitored by the Industrial Internet of Things (IIoTs) generate large amounts of Multivariate Time Series (MTS) data through heterogeneous sensor networks. While this data facilitates condition monitoring and anomaly detection, the increasing complexity and interdependencies within the sensor network pose significant challenges for anomaly detection. Despite progress in this field, much of the focus has been on point anomalies and contextual anomalies, with lesser attention paid to collective anomalies. A less addressed but common variant of collective anomalies is when the abnormal collective behavior is caused by shifts in interrelationships within the system. This can be due to abnormal environmental conditions like overheating, improper operational settings resulting from cyber-physical attacks, or system-level faults. To address these challenges, this paper proposes DyGATAD (Dynamic Graph Attention for Anomaly Detection), a graph-based anomaly detection framework that leverages the attention mechanism to construct a continuous graph representation of multivariate time series by inferring dynamic edges between time series. DyGATAD incorporates an operating condition-aware reconstruction combined with a topology-based anomaly score, thereby enhancing the detection ability of relationship shifts. We evaluate the performance of DyGATAD using both a synthetic dataset with controlled varying fault severity levels and an industrial-scale multiphase flow facility benchmark featuring various fault types with different detection difficulties. Our proposed approach demonstrated superior performance in collective anomaly detection for sensor networks, showing particular strength in early-stage fault detection, even in the case of faults with minimal severity.	['Olga Fink', 'Mengjie Zhao']	2023-07-07	null	null	null	null	['graph-attention', 'anomaly-detection', 'fault-detection']	['graphs', 'methodology', 'miscellaneous']	[ 3.91518831e-01 4.45751697e-02 1.77685112e-01 1.17321447e-01 -5.57114147e-02 -5.61815083e-01 3.51915807e-01 9.26114321e-01 4.61647034e-01 4.79513764e-01 -9.96495187e-02 -3.54702383e-01 -7.49522030e-01 -9.31004107e-01 -5.02201200e-01 -5.51747799e-01 -7.62950361e-01 3.23050797e-01 3.15269738e-01 -2.89125800e-01 2.77026445e-01 7.73410976e-01 -1.48391962e+00 -1.18740603e-01 7.62261569e-01 1.10177946e+00 -2.97908455e-01 5.76177001e-01 1.10522538e-01 5.55480421e-01 -1.09311080e+00 3.37584853e-01 2.85104603e-01 -2.19548240e-01 -5.97888291e-01 5.37675142e-01 -1.71067789e-01 1.08383104e-01 -3.30764562e-01 1.00731552e+00 2.42859527e-01 8.60159844e-02 2.55663991e-01 -1.77343261e+00 -1.31050751e-01 4.13497984e-01 -5.82658291e-01 7.76679158e-01 5.10339677e-01 4.82357085e-01 8.51794004e-01 -5.04974544e-01 3.17098707e-01 1.00212204e+00 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617abf86-c6c5-40bf-aab8-b90d713a6d61	unsupervised-cross-lingual-representation-1	1911.02116	null	https://arxiv.org/abs/1911.02116v2	https://arxiv.org/pdf/1911.02116v2.pdf	Unsupervised Cross-lingual Representation Learning at Scale	This paper shows that pretraining multilingual language models at scale leads to significant performance gains for a wide range of cross-lingual transfer tasks. We train a Transformer-based masked language model on one hundred languages, using more than two terabytes of filtered CommonCrawl data. Our model, dubbed XLM-R, significantly outperforms multilingual BERT (mBERT) on a variety of cross-lingual benchmarks, including +14.6% average accuracy on XNLI, +13% average F1 score on MLQA, and +2.4% F1 score on NER. XLM-R performs particularly well on low-resource languages, improving 15.7% in XNLI accuracy for Swahili and 11.4% for Urdu over previous XLM models. We also present a detailed empirical analysis of the key factors that are required to achieve these gains, including the trade-offs between (1) positive transfer and capacity dilution and (2) the performance of high and low resource languages at scale. Finally, we show, for the first time, the possibility of multilingual modeling without sacrificing per-language performance; XLM-R is very competitive with strong monolingual models on the GLUE and XNLI benchmarks. We will make our code, data and models publicly available.	['Francisco Guzmán', 'Vishrav Chaudhary', 'Kartikay Khandelwal', 'Alexis Conneau', 'Myle Ott', 'Luke Zettlemoyer', 'Guillaume Wenzek', 'Edouard Grave', 'Veselin Stoyanov', 'Naman Goyal']	2019-11-05	unsupervised-cross-lingual-representation-2	https://aclanthology.org/2020.acl-main.747	https://aclanthology.org/2020.acl-main.747.pdf	acl-2020-6	['multilingual-nlp']	['natural-language-processing']	[-6.58531010e-01 -2.12673113e-01 -4.63324040e-01 -3.26200753e-01 -1.80716300e+00 -8.27063143e-01 6.85899913e-01 -4.28233622e-03 -7.90341973e-01 9.56023276e-01 2.42874965e-01 -8.24525714e-01 3.67789775e-01 -6.42308116e-01 -1.07303822e+00 -1.85143173e-01 -1.89560309e-01 5.99725008e-01 9.72770900e-02 -5.55073917e-01 -2.56646097e-01 2.59471595e-01 -9.04848874e-01 3.48345131e-01 1.16286385e+00 5.32154143e-01 2.88740247e-01 4.24269885e-01 -2.18025893e-01 8.60337973e-01 -3.89633566e-01 -7.23899662e-01 2.90567517e-01 -5.89342080e-02 -7.68382728e-01 -4.82632786e-01 6.39367759e-01 -8.31483454e-02 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4d6bbe70-45d7-4de6-aa42-30e94844053b	spiking-two-stream-methods-with-unsupervised	2306.13783	null	https://arxiv.org/abs/2306.13783v1	https://arxiv.org/pdf/2306.13783v1.pdf	Spiking Two-Stream Methods with Unsupervised STDP-based Learning for Action Recognition	Video analysis is a computer vision task that is useful for many applications like surveillance, human-machine interaction, and autonomous vehicles. Deep Convolutional Neural Networks (CNNs) are currently the state-of-the-art methods for video analysis. However they have high computational costs, and need a large amount of labeled data for training. In this paper, we use Convolutional Spiking Neural Networks (CSNNs) trained with the unsupervised Spike Timing-Dependent Plasticity (STDP) learning rule for action classification. These networks represent the information using asynchronous low-energy spikes. This allows the network to be more energy efficient and neuromorphic hardware-friendly. However, the behaviour of CSNNs is not studied enough with spatio-temporal computer vision models. Therefore, we explore transposing two-stream neural networks into the spiking domain. Implementing this model with unsupervised STDP-based CSNNs allows us to further study the performance of these networks with video analysis. In this work, we show that two-stream CSNNs can successfully extract spatio-temporal information from videos despite using limited training data, and that the spiking spatial and temporal streams are complementary. We also show that using a spatio-temporal stream within a spiking STDP-based two-stream architecture leads to information redundancy and does not improve the performance.	['Ioan Marius Bilasco', 'Pierre Tirilly', 'Mireille El-Assal']	2023-06-23	null	null	null	null	['action-classification', 'autonomous-vehicles', 'action-recognition-in-videos']	['computer-vision', 'computer-vision', 'computer-vision']	[ 5.92971027e-01 -3.76197428e-01 1.15552358e-01 5.85631542e-02 1.64307043e-01 -3.84760529e-01 6.27538979e-01 3.74389328e-02 -6.78646743e-01 6.97078526e-01 -3.79809767e-01 -3.19059892e-03 8.20849016e-02 -8.31963480e-01 -1.06747842e+00 -1.09259498e+00 -1.92842156e-01 -2.57510036e-01 1.08038509e+00 -1.20963655e-01 2.46812269e-01 5.62758565e-01 -2.03174949e+00 7.40463197e-01 3.67520869e-01 1.30395508e+00 3.96343917e-01 6.56476140e-01 -1.27497926e-01 9.14319277e-01 -3.59721929e-01 2.17908353e-01 1.82195649e-01 -8.70914459e-01 -1.84777096e-01 -3.63194764e-01 -8.28308240e-02 1.20148912e-01 -7.31592357e-01 8.25032890e-01 2.90837467e-01 -1.88836321e-01 4.63839293e-01 -1.39072454e+00 -2.56507158e-01 3.12000394e-01 -4.73644584e-02 4.83575344e-01 -1.73329011e-01 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8.88204932e-01 5.88668108e-01 6.54932737e-01 3.24893624e-01 -1.15893352e+00 2.94353455e-01 -5.46880364e-01 -4.29310858e-01 1.84026454e-02 2.03894913e-01 -5.18811941e-01 -2.14160174e-01 5.72779775e-01]	[8.222579956054688, 2.4170591831207275]
6ecb1ff8-4b8f-4f6d-9537-7f0b1f67bfc8	latency-control-for-keyword-spotting	2206.07261	null	https://arxiv.org/abs/2206.07261v1	https://arxiv.org/pdf/2206.07261v1.pdf	Latency Control for Keyword Spotting	Conversational agents commonly utilize keyword spotting (KWS) to initiate voice interaction with the user. For user experience and privacy considerations, existing approaches to KWS largely focus on accuracy, which can often come at the expense of introduced latency. To address this tradeoff, we propose a novel approach to control KWS model latency and which generalizes to any loss function without explicit knowledge of the keyword endpoint. Through a single, tunable hyperparameter, our approach enables one to balance detection latency and accuracy for the targeted application. Empirically, we show that our approach gives superior performance under latency constraints when compared to existing methods. Namely, we make a substantial 25\% relative false accepts improvement for a fixed latency target when compared to the baseline state-of-the-art. We also show that when our approach is used in conjunction with a max-pooling loss, we are able to improve relative false accepts by 25 % at a fixed latency when compared to cross entropy loss.	['Brian Kulis', 'Yuriy Mishchenko', 'Mohammad Omar Khursheed', 'Grant P. Strimel', 'Joseph Wang', 'Christin Jose']	2022-06-15	null	null	null	null	['keyword-spotting']	['speech']	[ 8.92030448e-02 1.61206126e-01 -2.37833232e-01 -4.10586208e-01 -1.25170231e+00 -7.34746993e-01 7.22363591e-01 3.06723952e-01 -9.03490245e-01 5.45462251e-01 4.79474403e-02 -3.46061289e-01 2.04565346e-01 -3.68699849e-01 -4.09192920e-01 -4.79820102e-01 1.15891192e-02 3.17052037e-01 2.80728728e-01 4.76038828e-02 9.67583060e-02 2.81104922e-01 -1.11268294e+00 2.39913002e-01 7.09926248e-01 1.12638688e+00 -2.18737006e-01 8.09450567e-01 1.58399865e-02 5.43099225e-01 -8.03909838e-01 -5.36325395e-01 3.08489144e-01 -1.31472036e-01 -9.56810534e-01 -2.52025962e-01 2.00963572e-01 -6.27457857e-01 -2.47348681e-01 6.78478539e-01 7.48664558e-01 1.92868724e-01 3.57974708e-01 -1.43437755e+00 6.93881512e-02 6.02317870e-01 -2.65258193e-01 1.05297968e-01 5.06171346e-01 3.24611217e-01 1.30789745e+00 -6.47225320e-01 2.82514870e-01 1.07208884e+00 6.77710056e-01 5.66673458e-01 -1.73997808e+00 -5.21027625e-01 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4619ab15-6a13-468d-82ad-b5a2f3b1c897	textdefense-adversarial-text-detection-based	2302.05892	null	https://arxiv.org/abs/2302.05892v1	https://arxiv.org/pdf/2302.05892v1.pdf	TextDefense: Adversarial Text Detection based on Word Importance Entropy	Currently, natural language processing (NLP) models are wildly used in various scenarios. However, NLP models, like all deep models, are vulnerable to adversarially generated text. Numerous works have been working on mitigating the vulnerability from adversarial attacks. Nevertheless, there is no comprehensive defense in existing works where each work targets a specific attack category or suffers from the limitation of computation overhead, irresistible to adaptive attack, etc. In this paper, we exhaustively investigate the adversarial attack algorithms in NLP, and our empirical studies have discovered that the attack algorithms mainly disrupt the importance distribution of words in a text. A well-trained model can distinguish subtle importance distribution differences between clean and adversarial texts. Based on this intuition, we propose TextDefense, a new adversarial example detection framework that utilizes the target model's capability to defend against adversarial attacks while requiring no prior knowledge. TextDefense differs from previous approaches, where it utilizes the target model for detection and thus is attack type agnostic. Our extensive experiments show that TextDefense can be applied to different architectures, datasets, and attack methods and outperforms existing methods. We also discover that the leading factor influencing the performance of TextDefense is the target model's generalizability. By analyzing the property of the target model and the property of the adversarial example, we provide our insights into the adversarial attacks in NLP and the principles of our defense method.	['Yanghe Feng', 'Xing Yang', 'Chunpeng Ge', 'Yuwen Pu', 'Shouling Ji', 'Xuhong Zhang', 'Lujia Shen']	2023-02-12	null	null	null	null	['adversarial-text']	['adversarial']	[ 1.32105485e-01 -1.10408925e-01 7.62715340e-02 -1.33392587e-01 -6.54040992e-01 -1.34876204e+00 8.79595697e-01 1.67911932e-01 -5.92300259e-02 3.61345917e-01 4.00394946e-01 -5.32046914e-01 1.91102087e-01 -1.00679302e+00 -6.77973092e-01 -5.44658840e-01 3.63493823e-02 4.44969498e-02 1.65587470e-01 -5.60020506e-01 2.76620775e-01 6.05571389e-01 -8.10431778e-01 4.76196378e-01 7.97572672e-01 6.15564466e-01 -3.37444156e-01 7.90640891e-01 -3.25889021e-01 7.63618827e-01 -1.12787485e+00 -8.97991240e-01 7.53260493e-01 -1.48333341e-01 -7.19140112e-01 -6.98123515e-01 4.93995726e-01 -3.69876653e-01 -8.01903784e-01 1.50950205e+00 6.90277696e-01 -6.58218265e-02 6.54136002e-01 -1.36102629e+00 -1.04109025e+00 1.14232647e+00 -4.44954753e-01 5.08747756e-01 4.27487135e-01 5.65524101e-01 8.34104598e-01 -5.92742085e-01 2.11213931e-01 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1a97341f-76f1-49d1-8957-95686c9fdade	toward-multi-target-self-organizing-pursuit	2206.12330	null	https://arxiv.org/abs/2206.12330v3	https://arxiv.org/pdf/2206.12330v3.pdf	Toward multi-target self-organizing pursuit in a partially observable Markov game	The multiple-target self-organizing pursuit (SOP) problem has wide applications and has been considered a challenging self-organization game for distributed systems, in which intelligent agents cooperatively pursue multiple dynamic targets with partial observations. This work proposes a framework for decentralized multi-agent systems to improve the implicit coordination capabilities in search and pursuit. We model a self-organizing system as a partially observable Markov game (POMG) featured by large-scale, decentralization, partial observation, and noncommunication. The proposed distributed algorithm: fuzzy self-organizing cooperative coevolution (FSC2) is then leveraged to resolve the three challenges in multi-target SOP: distributed self-organizing search (SOS), distributed task allocation, and distributed single-target pursuit. FSC2 includes a coordinated multi-agent deep reinforcement learning (MARL) method that enables homogeneous agents to learn natural SOS patterns. Additionally, we propose a fuzzy-based distributed task allocation method, which locally decomposes multi-target SOP into several single-target pursuit problems. The cooperative coevolution principle is employed to coordinate distributed pursuers for each single-target pursuit problem. Therefore, the uncertainties of inherent partial observation and distributed decision-making in the POMG can be alleviated. The experimental results demonstrate that by decomposing the SOP task, FSC2 achieves superior performance compared with other implicit coordination policies fully trained by general MARL algorithms. The scalability of FSC2 is proved that up to 2048 FSC2 agents perform efficient multi-target SOP with almost 100 percent capture rates. Empirical analyses and ablation studies verify the interpretability, rationality, and effectiveness of component algorithms in FSC2.	['Chin-Teng Lin', 'Yuhui Shi', 'Ye Shi', 'Chao Lyu', 'Yu-Cheng Chang', 'Lijun Sun']	2022-06-24	null	null	null	null	['multi-agent-path-finding']	['playing-games']	[-4.65391040e-01 1.35185085e-02 -1.14086419e-01 3.80970359e-01 -5.06710649e-01 -5.16894102e-01 4.44039971e-01 -2.28163928e-01 -2.17267990e-01 7.26469159e-01 -5.77073209e-02 1.64054960e-01 -8.00097346e-01 -5.40894687e-01 -2.89649993e-01 -1.38308370e+00 -5.88132918e-01 1.00664437e+00 2.09204748e-01 -6.18190050e-01 5.63532710e-02 2.24449635e-01 -1.57089925e+00 -3.17921788e-01 1.35896206e+00 9.12546933e-01 6.44995749e-01 5.68361998e-01 4.64263648e-01 1.02037156e+00 -6.50861084e-01 2.77954072e-01 6.27579153e-01 -2.32044652e-01 -2.33180225e-01 2.41048276e-01 -5.63190341e-01 -4.48958613e-02 -2.01245502e-01 1.22491813e+00 5.96054494e-01 3.90291631e-01 7.43994474e-01 -1.83244848e+00 -9.86253679e-01 4.17939097e-01 -6.86432779e-01 1.65149808e-01 -2.04909563e-01 5.07389903e-01 7.82043278e-01 -4.23817754e-01 3.60763550e-01 1.34884524e+00 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23b5aa20-ffe7-4976-b977-880c34b1f75d	hypercon-image-to-video-model-transfer-for	1912.04950	null	https://arxiv.org/abs/1912.04950v2	https://arxiv.org/pdf/1912.04950v2.pdf	HyperCon: Image-To-Video Model Transfer for Video-To-Video Translation Tasks	Video-to-video translation is more difficult than image-to-image translation due to the temporal consistency problem that, if unaddressed, leads to distracting flickering effects. Although video models designed from scratch produce temporally consistent results, training them to match the vast visual knowledge captured by image models requires an intractable number of videos. To combine the benefits of image and video models, we propose an image-to-video model transfer method called Hyperconsistency (HyperCon) that transforms any well-trained image model into a temporally consistent video model without fine-tuning. HyperCon works by translating a temporally interpolated video frame-wise and then aggregating over temporally localized windows on the interpolated video. It handles both masked and unmasked inputs, enabling support for even more video-to-video translation tasks than prior image-to-video model transfer techniques. We demonstrate HyperCon on video style transfer and inpainting, where it performs favorably compared to prior state-of-the-art methods without training on a single stylized or incomplete video. Our project website is available at https://ryanszeto.com/projects/hypercon .	['Mostafa El-Khamy', 'Jungwon Lee', 'Ryan Szeto', 'Jason J. Corso']	2019-12-10	null	null	null	null	['video-style-transfer', 'video-inpainting']	['computer-vision', 'computer-vision']	[ 3.90134454e-01 -4.96944450e-02 -3.53984505e-01 -1.23884499e-01 -9.77502048e-01 -7.14831948e-01 6.53348863e-01 -7.73938060e-01 -1.12269912e-02 7.47541726e-01 2.34109417e-01 -1.45010576e-01 6.43078208e-01 -2.84759641e-01 -1.38954973e+00 -2.85671383e-01 2.61880964e-01 1.42011181e-01 2.02324867e-01 2.01747447e-01 1.10227123e-01 1.27709597e-01 -1.49541163e+00 8.36044729e-01 4.92472768e-01 7.13548362e-01 3.43707472e-01 1.00781488e+00 -5.31797670e-02 1.20004523e+00 -3.83640051e-01 -2.26771504e-01 2.64942646e-01 -9.29260492e-01 -6.38059378e-01 5.02818346e-01 1.07668364e+00 -7.16177821e-01 -5.12418449e-01 7.77798474e-01 1.63983792e-01 7.26697743e-02 4.65379208e-01 -1.52152431e+00 -1.13982332e+00 1.95039913e-01 -6.26998603e-01 1.45811453e-01 5.83404005e-01 3.55597854e-01 4.32416141e-01 -1.23246717e+00 1.21339679e+00 1.29730034e+00 3.94113481e-01 7.47440159e-01 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7.58139908e-01 5.10168314e-01 -4.39325392e-01 -1.32596999e-01 9.53415096e-01]	[10.871296882629395, -0.6865136027336121]
de8c6bed-3fcd-4665-b5ac-092ad5303eb7	incremental-deep-learning-for-robust-object	1810.10323	null	http://arxiv.org/abs/1810.10323v1	http://arxiv.org/pdf/1810.10323v1.pdf	Incremental Deep Learning for Robust Object Detection in Unknown Cluttered Environments	Object detection in streaming images is a major step in different detection-based applications, such as object tracking, action recognition, robot navigation, and visual surveillance applications. In mostcases, image quality is noisy and biased, and as a result, the data distributions are disturbed and imbalanced. Most object detection approaches, such as the faster region-based convolutional neural network (Faster RCNN), Single Shot Multibox Detector with 300x300 inputs (SSD300), and You Only Look Once version 2 (YOLOv2), rely on simple sampling without considering distortions and noise under real-world changing environments, despite poor object labeling. In this paper, we propose an Incremental active semi-supervised learning (IASSL) technology for unseen object detection. It combines batch-based active learning (AL) and bin-based semi-supervised learning (SSL) to leverage the strong points of AL's exploration and SSL's exploitation capabilities. A collaborative sampling method is also adopted to measure the uncertainty and diversity of AL and the confidence in SSL. Batch-based AL allows us to select more informative, confident, and representative samples with low cost. Bin-based SSL divides streaming image samples into several bins, and each bin repeatedly transfers the discriminative knowledge of convolutional neural network (CNN) deep learning to the next bin until the performance criterion is reached. IASSL can overcome noisy and biased labels in unknown, cluttered data distributions. We obtain superior performance, compared to state-of-the-art technologies such as Faster RCNN, SSD300, and YOLOv2.	['Rhee Phill Kyu', 'Ahmed Minhaz Uddin', 'Shin Dong Kyun']	2018-10-13	null	null	null	null	['robust-object-detection']	['computer-vision']	[ 1.49596378e-01 -8.73757526e-02 -5.17617464e-01 -3.18794996e-01 -6.77177608e-01 -3.60709280e-01 4.17720824e-01 1.33808091e-01 -5.67473352e-01 6.52561307e-01 -3.55513006e-01 5.13038151e-02 -1.44703507e-01 -6.60019636e-01 -7.80651093e-01 -1.15524578e+00 3.89850475e-02 2.97504067e-01 8.76649916e-01 2.62316912e-01 -1.82102658e-02 6.91282034e-01 -1.75264978e+00 2.55979091e-01 7.98843443e-01 1.65206063e+00 2.45704859e-01 5.86228013e-01 -3.71003091e-01 7.62196362e-01 -7.27462828e-01 -3.06121316e-02 2.60767847e-01 -9.05823857e-02 -1.42552078e-01 -4.57933079e-03 -9.66174249e-03 -6.05844378e-01 -1.53988540e-01 1.29913652e+00 4.80929911e-01 7.93744624e-02 4.59879071e-01 -1.57351732e+00 -3.27235609e-01 6.49657667e-01 -6.99752688e-01 4.83772337e-01 -1.52803540e-01 5.60067773e-01 4.02047932e-01 -9.56122458e-01 2.12479636e-01 1.28094995e+00 6.00056171e-01 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b34596f6-12b3-4f77-98a1-ce910ef2dbc8	fast-matrix-multiplication-without-tears-a	2306.01097	null	https://arxiv.org/abs/2306.01097v1	https://arxiv.org/pdf/2306.01097v1.pdf	Fast Matrix Multiplication Without Tears: A Constraint Programming Approach	It is known that the multiplication of an $N \times M$ matrix with an $M \times P$ matrix can be performed using fewer multiplications than what the naive $NMP$ approach suggests. The most famous instance of this is Strassen's algorithm for multiplying two $2\times 2$ matrices in 7 instead of 8 multiplications. This gives rise to the constraint satisfaction problem of fast matrix multiplication, where a set of $R < NMP$ multiplication terms must be chosen and combined such that they satisfy correctness constraints on the output matrix. Despite its highly combinatorial nature, this problem has not been exhaustively examined from that perspective, as evidenced for example by the recent deep reinforcement learning approach of AlphaTensor. In this work, we propose a simple yet novel Constraint Programming approach to find non-commutative algorithms for fast matrix multiplication or provide proof of infeasibility otherwise. We propose a set of symmetry-breaking constraints and valid inequalities that are particularly helpful in proving infeasibility. On the feasible side, we find that exploiting solver performance variability in conjunction with a sparsity-based problem decomposition enables finding solutions for larger (feasible) instances of fast matrix multiplication. Our experimental results using CP Optimizer demonstrate that we can find fast matrix multiplication algorithms for matrices up to $3\times 3$ in a short amount of time.	['Elias B. Khalil', 'Pashootan Vaezipoor', 'Chang Liu', 'Arnaud Deza']	2023-06-01	null	null	null	null	['problem-decomposition']	['miscellaneous']	[ 1.54585168e-01 9.65838209e-02 -8.23723339e-03 -1.37539163e-01 -5.18988252e-01 -6.91892385e-01 3.56207564e-02 1.44369513e-01 -5.30605555e-01 9.16640520e-01 -4.79199618e-01 -7.92192698e-01 -6.34705603e-01 -8.85071695e-01 -1.05066741e+00 -6.97885573e-01 -7.25399554e-01 4.72667187e-01 -3.79552901e-01 -5.72875440e-01 4.63519394e-01 3.76560181e-01 -1.52297413e+00 3.11229140e-01 7.63233006e-01 1.15330029e+00 -6.14160113e-03 5.53278685e-01 -8.01754892e-02 4.07116145e-01 -4.28764492e-01 -4.87521678e-01 9.48491633e-01 -3.31497043e-01 -9.33556497e-01 -8.00703466e-03 4.42578018e-01 -1.18131518e-01 1.18469916e-01 1.03654385e+00 -9.76129156e-03 3.59326117e-02 1.56765997e-01 -1.62248397e+00 3.65132513e-03 9.78621244e-01 -1.06391466e+00 1.05596900e-01 4.36267316e-01 2.80730069e-01 1.49915016e+00 -4.58407313e-01 5.30858219e-01 8.52953911e-01 4.52762574e-01 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b367c441-67de-40aa-add0-dac4f6b46881	deep-sequential-segmentation-of-organs-in	1807.02437	null	http://arxiv.org/abs/1807.02437v2	http://arxiv.org/pdf/1807.02437v2.pdf	Deep Sequential Segmentation of Organs in Volumetric Medical Scans	Segmentation in 3D scans is playing an increasingly important role in current clinical practice supporting diagnosis, tissue quantification, or treatment planning. The current 3D approaches based on convolutional neural networks usually suffer from at least three main issues caused predominantly by implementation constraints - first, they require resizing the volume to the lower-resolutional reference dimensions, second, the capacity of such approaches is very limited due to memory restrictions, and third, all slices of volumes have to be available at any given training or testing time. We address these problems by a U-Net-like architecture consisting of bidirectional convolutional LSTM and convolutional, pooling, upsampling and concatenation layers enclosed into time-distributed wrappers. Our network can either process the full volumes in a sequential manner, or segment slabs of slices on demand. We demonstrate performance of our architecture on vertebrae and liver segmentation tasks in 3D CT scans.	['Katja Bühler', 'Maria Wimmer', 'Dimitrios Lenis', 'Alexey Novikov', 'David Major']	2018-07-06	null	null	null	null	['liver-segmentation']	['medical']	[ 1.74821422e-01 1.35351807e-01 -2.09716499e-01 -4.45778400e-01 -5.87040782e-01 -2.91435510e-01 2.59722292e-01 3.55618387e-01 -4.92588311e-01 5.32434106e-01 1.87609524e-01 -7.27738082e-01 8.09102952e-02 -9.21272635e-01 -6.03107274e-01 -4.74123895e-01 -4.47331041e-01 5.68008184e-01 5.15559077e-01 3.15409034e-01 -1.06461123e-01 9.59061861e-01 -6.70387506e-01 4.05529082e-01 4.45384115e-01 1.36206615e+00 4.69808886e-03 9.05543089e-01 -4.67032403e-01 7.46685922e-01 -2.88528234e-01 2.51435116e-02 4.37518209e-01 -4.02464211e-01 -1.13057327e+00 2.44312450e-01 1.87966619e-02 -7.83292294e-01 -4.46053237e-01 8.04669619e-01 7.26124763e-01 1.38412761e-02 4.77217436e-01 -6.94856703e-01 -2.99337804e-01 6.89741790e-01 -5.45365632e-01 3.29986691e-01 -1.30138904e-01 -1.91462506e-03 2.89405048e-01 -5.52152693e-01 4.81440842e-01 8.59056413e-01 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-6.37780070e-01 -1.00001979e+00 -4.32892025e-01 5.28154522e-02 4.16878462e-01 -1.87914118e-01 1.88971296e-01 -2.07976013e-01]	[14.490823745727539, -2.5437774658203125]
86e6fad4-49af-4b0c-9eef-a22b7f119fe6	saltinet-scan-path-prediction-on-360-degree	1707.03123	null	http://arxiv.org/abs/1707.03123v5	http://arxiv.org/pdf/1707.03123v5.pdf	SaltiNet: Scan-path Prediction on 360 Degree Images using Saliency Volumes	We introduce SaltiNet, a deep neural network for scanpath prediction trained on 360-degree images. The model is based on a temporal-aware novel representation of saliency information named the saliency volume. The first part of the network consists of a model trained to generate saliency volumes, whose parameters are fit by back-propagation computed from a binary cross entropy (BCE) loss over downsampled versions of the saliency volumes. Sampling strategies over these volumes are used to generate scanpaths over the 360-degree images. Our experiments show the advantages of using saliency volumes, and how they can be used for related tasks. Our source code and trained models available at https://github.com/massens/saliency-360salient-2017.	["Noel E. O'Connor", 'Xavier Giro-i-Nieto', 'Kevin McGuinness', 'Marc Assens']	2017-07-11	null	null	null	null	['scanpath-prediction']	['computer-vision']	[ 2.06148788e-01 4.45254624e-01 -4.42953378e-01 -6.36453450e-01 -6.54022336e-01 -1.16785049e-01 5.55493534e-01 -1.91351473e-01 -5.67934057e-03 3.46119881e-01 5.50334275e-01 -2.68077791e-01 1.27856480e-02 -9.42948580e-01 -1.08682728e+00 -2.00237259e-01 -2.74086952e-01 2.08670363e-01 6.56713486e-01 -4.05192405e-01 5.70251048e-01 4.97866929e-01 -1.61015880e+00 1.79952309e-01 9.82492149e-01 1.00566661e+00 3.65672320e-01 6.48060560e-01 2.81941146e-01 7.66173661e-01 -2.43632928e-01 -2.86174119e-01 6.75454378e-01 -1.69151828e-01 -8.79082918e-01 -3.73471320e-01 7.51820028e-01 -5.79098761e-01 -6.36594713e-01 8.83477330e-01 3.25420588e-01 2.75267839e-01 5.94286442e-01 -1.14729512e+00 -5.28022587e-01 7.16007590e-01 -6.15462661e-01 5.89867115e-01 1.21450424e-01 2.83520043e-01 1.13792241e+00 -1.01122749e+00 8.18351626e-01 1.14716482e+00 7.24908590e-01 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39adc51d-2ae0-44fa-8205-0f508d57772c	diversity-aware-coherence-loss-for-improving	2305.16199	null	https://arxiv.org/abs/2305.16199v2	https://arxiv.org/pdf/2305.16199v2.pdf	Diversity-Aware Coherence Loss for Improving Neural Topic Models	The standard approach for neural topic modeling uses a variational autoencoder (VAE) framework that jointly minimizes the KL divergence between the estimated posterior and prior, in addition to the reconstruction loss. Since neural topic models are trained by recreating individual input documents, they do not explicitly capture the coherence between topic words on the corpus level. In this work, we propose a novel diversity-aware coherence loss that encourages the model to learn corpus-level coherence scores while maintaining a high diversity between topics. Experimental results on multiple datasets show that our method significantly improves the performance of neural topic models without requiring any pretraining or additional parameters.	['Giuseppe Carenini', 'Gabriel Murray', 'Linzi Xing', 'Felipe González-Pizarro', 'Raymond Li']	2023-05-25	null	null	null	null	['topic-models']	['natural-language-processing']	[-2.66994596e-01 4.42870438e-01 -2.38382325e-01 -5.01734853e-01 -8.62835586e-01 -9.13246274e-02 8.44531596e-01 9.08896048e-03 -1.57537133e-01 6.29622877e-01 4.88564372e-01 6.91035911e-02 1.05711661e-01 -7.72935987e-01 -8.47555339e-01 -7.73512900e-01 1.83962315e-01 6.44966543e-01 8.47531110e-02 9.69182253e-02 -7.52819031e-02 -3.68522108e-01 -1.36780226e+00 -4.89204712e-02 1.13219881e+00 7.40941823e-01 5.80820858e-01 2.82647967e-01 -2.88201362e-01 5.89216352e-01 -7.98240662e-01 -3.34312290e-01 -1.25100806e-01 -4.91710097e-01 -3.76741707e-01 2.66168714e-01 5.60644925e-01 -3.63507122e-01 -3.16125989e-01 1.02965844e+00 1.16466716e-01 5.00344634e-01 8.79548728e-01 -1.05025232e+00 -5.82112491e-01 8.80481064e-01 -3.23799908e-01 -9.85801369e-02 -3.80344659e-01 -2.50002086e-01 1.46921837e+00 -9.93701994e-01 7.12132275e-01 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2.88971782e-01 -6.33571148e-01 -5.09641767e-01 -1.35419583e+00 -6.76885724e-01 -8.93396959e-02 1.93300039e-01 -7.42484212e-01 -1.45586759e-01 1.65176973e-01]	[10.424806594848633, 6.980449676513672]
12f772a1-4f55-4ab1-8250-026850c038d7	improving-speech-translation-by-understanding	2107.05782	null	https://arxiv.org/abs/2107.05782v1	https://arxiv.org/pdf/2107.05782v1.pdf	Improving Speech Translation by Understanding and Learning from the Auxiliary Text Translation Task	Pretraining and multitask learning are widely used to improve the speech to text translation performance. In this study, we are interested in training a speech to text translation model along with an auxiliary text to text translation task. We conduct a detailed analysis to understand the impact of the auxiliary task on the primary task within the multitask learning framework. Our analysis confirms that multitask learning tends to generate similar decoder representations from different modalities and preserve more information from the pretrained text translation modules. We observe minimal negative transfer effect between the two tasks and sharing more parameters is helpful to transfer knowledge from the text task to the speech task. The analysis also reveals that the modality representation difference at the top decoder layers is still not negligible, and those layers are critical for the translation quality. Inspired by these findings, we propose three methods to improve translation quality. First, a parameter sharing and initialization strategy is proposed to enhance information sharing between the tasks. Second, a novel attention-based regularization is proposed for the encoders and pulls the representations from different modalities closer. Third, an online knowledge distillation is proposed to enhance the knowledge transfer from the text to the speech task. Our experiments show that the proposed approach improves translation performance by more than 2 BLEU over a strong baseline and achieves state-of-the-art results on the \textsc{MuST-C} English-German, English-French and English-Spanish language pairs.	['Dmitriy Genzel', 'Changhan Wang', 'Xian Li', 'Juan Pino', 'Yun Tang']	2021-07-12	null	https://aclanthology.org/2021.acl-long.328	https://aclanthology.org/2021.acl-long.328.pdf	acl-2021-5	['speech-to-text-translation']	['natural-language-processing']	[ 2.35947192e-01 9.03306678e-02 -3.14385772e-01 -3.20509821e-01 -1.35760546e+00 -4.67255741e-01 8.15056920e-01 -3.19574505e-01 -5.14572263e-01 8.35479200e-01 6.84280872e-01 -4.06169057e-01 3.38585794e-01 -3.12286973e-01 -1.00552022e+00 -8.04839969e-01 7.67429888e-01 4.78600472e-01 -7.19198808e-02 -4.43364114e-01 -1.15804203e-01 -1.85040027e-01 -8.25614035e-01 8.22468817e-01 1.22787726e+00 6.09392107e-01 6.64609492e-01 2.48393044e-01 -2.77616620e-01 5.37371457e-01 -5.46555579e-01 -7.90624082e-01 1.05202965e-01 -7.37921715e-01 -8.29852402e-01 -8.42111334e-02 1.98791981e-01 -2.53370255e-01 -2.90842235e-01 9.06061172e-01 8.30297410e-01 1.98639452e-01 7.17030942e-01 -8.87892425e-01 -8.69102776e-01 1.01379311e+00 -5.66399455e-01 5.48235625e-02 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247a2e33-01ab-4a8f-bee5-cc73dbe4f4bd	ransomai-ai-powered-ransomware-for-stealthy	2306.15559	null	https://arxiv.org/abs/2306.15559v1	https://arxiv.org/pdf/2306.15559v1.pdf	RansomAI: AI-powered Ransomware for Stealthy Encryption	Cybersecurity solutions have shown promising performance when detecting ransomware samples that use fixed algorithms and encryption rates. However, due to the current explosion of Artificial Intelligence (AI), sooner than later, ransomware (and malware in general) will incorporate AI techniques to intelligently and dynamically adapt its encryption behavior to be undetected. It might result in ineffective and obsolete cybersecurity solutions, but the literature lacks AI-powered ransomware to verify it. Thus, this work proposes RansomAI, a Reinforcement Learning-based framework that can be integrated into existing ransomware samples to adapt their encryption behavior and stay stealthy while encrypting files. RansomAI presents an agent that learns the best encryption algorithm, rate, and duration that minimizes its detection (using a reward mechanism and a fingerprinting intelligent detection system) while maximizing its damage function. The proposed framework was validated in a ransomware, Ransomware-PoC, that infected a Raspberry Pi 4, acting as a crowdsensor. A pool of experiments with Deep Q-Learning and Isolation Forest (deployed on the agent and detection system, respectively) has demonstrated that RansomAI evades the detection of Ransomware-PoC affecting the Raspberry Pi 4 in a few minutes with >90% accuracy.	['Burkhard Stiller', 'Gregorio Martínez Pérez', 'Gérôme Bovet', 'Pedro Miguel Sánchez Sánchez', 'Janik Luechinger', 'Alberto Huertas Celdrán', 'Jan von der Assen']	2023-06-27	null	null	null	null	['q-learning']	['methodology']	[-6.43869415e-02 -2.85428911e-01 1.69282164e-02 3.13602000e-01 4.95118767e-01 -1.04431629e+00 5.53611815e-01 -1.00738287e-01 -4.60233837e-01 6.86408162e-01 -4.84950721e-01 -4.64474857e-01 -3.17540079e-01 -7.73684204e-01 -3.53233665e-01 -6.98703706e-01 -1.15193069e-01 7.09754467e-01 1.75714239e-01 -1.76857665e-01 5.23778915e-01 7.57734358e-01 -1.43761432e+00 -3.71569470e-02 6.77279472e-01 1.12363601e+00 -2.50209868e-01 1.01127219e+00 6.63533270e-01 8.94725263e-01 -1.10249794e+00 -2.70449638e-01 8.53020489e-01 -3.03151101e-01 -2.78157651e-01 -3.77197057e-01 -4.31277901e-02 -7.06013024e-01 -3.40665460e-01 8.00071120e-01 4.10168439e-01 -1.28063992e-01 5.79153836e-01 -1.88143575e+00 -8.65830958e-01 4.82686579e-01 -2.69284427e-01 2.63878316e-01 3.56007993e-01 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cff32782-e117-43db-b1b7-a87921abb67d	towards-better-characterization-of	null	null	https://openreview.net/forum?id=t2UJIFZVyz4	https://openreview.net/pdf?id=t2UJIFZVyz4	Towards Better Characterization of Paraphrases	To effectively characterize the nature of paraphrase pairs without expert human annotation, we proposes two new metrics: word position deviation (WPD) and lexical deviation (LD). WPD measures the degree of structural alteration, while LD measures the difference in vocabulary used. We apply these metrics to better understand the commonly-used MRPC dataset and study how it differs from PAWS, another paraphrase identification dataset. We also perform a detailed study on MRPC and propose improvements to the dataset, showing that it improves generalizability of models trained on the dataset. Lastly, we apply our metrics to filter the output of a paraphrase generation model and show how it can be used to generate specific forms of paraphrases for data augmentation or robustness testing of NLP models.	['Anonymous']	2021-09-17	null	null	null	acl-arr-september-2021-9	['paraphrase-generation', 'paraphrase-identification', 'paraphrase-generation']	['computer-code', 'natural-language-processing', 'natural-language-processing']	[ 5.21799862e-01 -3.62944640e-02 -2.86716968e-01 -2.30377346e-01 -8.74551177e-01 -1.16411090e+00 6.72434032e-01 5.63707471e-01 -3.94551039e-01 5.58185637e-01 7.11941242e-01 -5.62044442e-01 -8.82438645e-02 -6.12799227e-01 -7.01704264e-01 -6.01618588e-02 6.20232284e-01 3.70892674e-01 2.08988309e-01 -3.11985999e-01 8.73442650e-01 5.25098026e-01 -1.49330211e+00 7.88249135e-01 8.86655986e-01 3.86507183e-01 9.82904881e-02 5.35710752e-01 -4.09546606e-02 4.65767503e-01 -8.03631067e-01 -4.83655006e-01 3.04373831e-01 -3.79322648e-01 -1.03287554e+00 -5.61904371e-01 5.82915485e-01 1.62466958e-01 -3.19820270e-02 7.92204618e-01 4.93835151e-01 -7.95357972e-02 8.14174116e-01 -1.08069646e+00 -9.17247593e-01 8.22185278e-01 -2.18155179e-02 5.23654282e-01 1.07237685e+00 2.59881407e-01 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59e15ac2-84e3-474e-ae81-8c6ce383e113	retrieving-to-answer-zero-shot-video-question	2306.11732	null	https://arxiv.org/abs/2306.11732v1	https://arxiv.org/pdf/2306.11732v1.pdf	Retrieving-to-Answer: Zero-Shot Video Question Answering with Frozen Large Language Models	Video Question Answering (VideoQA) has been significantly advanced from the scaling of recent Large Language Models (LLMs). The key idea is to convert the visual information into the language feature space so that the capacity of LLMs can be fully exploited. Existing VideoQA methods typically take two paradigms: (1) learning cross-modal alignment, and (2) using an off-the-shelf captioning model to describe the visual data. However, the first design needs costly training on many extra multi-modal data, whilst the second is further limited by limited domain generalization. To address these limitations, a simple yet effective Retrieving-to-Answer (R2A) framework is proposed.Given an input video, R2A first retrieves a set of semantically similar texts from a generic text corpus using a pre-trained multi-modal model (e.g., CLIP). With both the question and the retrieved texts, a LLM (e.g., DeBERTa) can be directly used to yield a desired answer. Without the need for cross-modal fine-tuning, R2A allows for all the key components (e.g., LLM, retrieval model, and text corpus) to plug-and-play. Extensive experiments on several VideoQA benchmarks show that despite with 1.3B parameters and no fine-tuning, our R2A can outperform the 61 times larger Flamingo-80B model even additionally trained on nearly 2.1B multi-modal data.	['Hongsheng Li', 'Yu Qiao', 'Yi Wang', 'Renrui Zhang', 'Xiatian Zhu', 'Yuying Ge', 'Ziyi Lin', 'Junting Pan']	2023-06-15	null	null	null	null	['video-question-answering', 'domain-generalization', 'question-answering']	['computer-vision', 'methodology', 'natural-language-processing']	[ 8.55148062e-02 -2.24641055e-01 -8.95929337e-02 -2.61809856e-01 -1.58454156e+00 -8.83075476e-01 6.47936404e-01 -6.42782524e-02 -3.78374994e-01 3.12936366e-01 3.15561622e-01 -3.17625433e-01 2.36434475e-01 -5.94970465e-01 -9.23285246e-01 -4.67243582e-01 3.84606630e-01 6.45472467e-01 3.79317194e-01 -3.64145219e-01 1.36696309e-01 -5.12796529e-02 -1.51336324e+00 7.46102512e-01 6.39222860e-01 1.23343110e+00 5.42640388e-01 8.65517795e-01 -7.04152942e-01 8.57216358e-01 -4.87314463e-01 -6.45501018e-01 2.43698880e-01 -5.47499776e-01 -9.37021375e-01 1.72858033e-02 8.30715239e-01 -6.13721311e-01 -4.45191950e-01 7.87700176e-01 4.39409614e-01 3.04272830e-01 6.29187942e-01 -1.31792879e+00 -7.69243479e-01 1.56655118e-01 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fa8f711c-8c88-4e38-aa6a-9ad846c44b6b	learning-depth-guided-convolutions-for	1912.04799	null	https://arxiv.org/abs/1912.04799v2	https://arxiv.org/pdf/1912.04799v2.pdf	Learning Depth-Guided Convolutions for Monocular 3D Object Detection	3D object detection from a single image without LiDAR is a challenging task due to the lack of accurate depth information. Conventional 2D convolutions are unsuitable for this task because they fail to capture local object and its scale information, which are vital for 3D object detection. To better represent 3D structure, prior arts typically transform depth maps estimated from 2D images into a pseudo-LiDAR representation, and then apply existing 3D point-cloud based object detectors. However, their results depend heavily on the accuracy of the estimated depth maps, resulting in suboptimal performance. In this work, instead of using pseudo-LiDAR representation, we improve the fundamental 2D fully convolutions by proposing a new local convolutional network (LCN), termed Depth-guided Dynamic-Depthwise-Dilated LCN (D$^4$LCN), where the filters and their receptive fields can be automatically learned from image-based depth maps, making different pixels of different images have different filters. D$^4$LCN overcomes the limitation of conventional 2D convolutions and narrows the gap between image representation and 3D point cloud representation. Extensive experiments show that D$^4$LCN outperforms existing works by large margins. For example, the relative improvement of D$^4$LCN against the state-of-the-art on KITTI is 9.1\% in the moderate setting. The code is available at https://github.com/dingmyu/D4LCN.	['Yuqi Huo', 'Zhiwu Lu', 'Mingyu Ding', 'Ping Luo', 'Hongwei Yi', 'Zhe Wang', 'Jianping Shi']	2019-12-10	learning-depth-guided-convolutions-for-1	http://openaccess.thecvf.com/content_CVPR_2020/html/Ding_Learning_Depth-Guided_Convolutions_for_Monocular_3D_Object_Detection_CVPR_2020_paper.html	http://openaccess.thecvf.com/content_CVPR_2020/papers/Ding_Learning_Depth-Guided_Convolutions_for_Monocular_3D_Object_Detection_CVPR_2020_paper.pdf	cvpr-2020-6	['vehicle-pose-estimation']	['computer-vision']	[ 6.90925196e-02 -2.06487507e-01 -8.49822536e-02 -2.74065077e-01 -3.59719068e-01 -4.84042585e-01 4.76747930e-01 -1.54725879e-01 -5.25306463e-01 1.68805912e-01 -2.45283231e-01 -4.01697159e-01 2.37735078e-01 -9.41123068e-01 -9.08313036e-01 -4.24398750e-01 8.87181163e-02 2.86533713e-01 7.36108661e-01 2.41292212e-02 2.46637151e-01 8.94353390e-01 -1.61899471e+00 1.02085717e-01 7.18426287e-01 1.43882072e+00 5.27758539e-01 3.75475436e-01 -5.72130144e-01 8.63474458e-02 -2.48877510e-01 -1.27493456e-01 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afdec513-f5f4-48c2-b1a2-81aef76a8bc5	updet-universal-multi-agent-rl-via-policy	null	null	https://openreview.net/forum?id=v9c7hr9ADKx	https://openreview.net/pdf?id=v9c7hr9ADKx	UPDeT: Universal Multi-agent RL via Policy Decoupling with Transformers	Recent advances in multi-agent reinforcement learning have been largely limited in training one model from scratch for every new task. The limitation is due to the restricted model architecture related to fixed input and output dimensions. This hinders the experience accumulation and transfer of the learned agent over tasks with diverse levels of difficulty (e.g. 3 vs 3 or 5 vs 6 multi-agent games). In this paper, we make the first attempt to explore a universal multi-agent reinforcement learning pipeline, designing one single architecture to fit tasks with the requirement of different observation and action configurations. Unlike previous RNN-based models, we utilize a transformer-based model to generate a flexible policy by decoupling the policy distribution from the intertwined input observation with an importance weight measured by the merits of the self-attention mechanism. Compared to a standard transformer block, the proposed model, named as Universal Policy Decoupling Transformer (UPDeT), further relaxes the action restriction and makes the multi-agent task's decision process more explainable. UPDeT is general enough to be plugged into any multi-agent reinforcement learning pipeline and equip them with strong generalization abilities that enables the handling of multiple tasks at a time. Extensive experiments on large-scale SMAC multi-agent competitive games demonstrate that the proposed UPDeT-based multi-agent reinforcement learning achieves significant results relative to state-of-the-art approaches, demonstrating advantageous transfer capability in terms of both performance and training speed (10 times faster).	['Xiaodan Liang', 'Xiaojun Chang', 'Fengda Zhu', 'Siyi Hu']	2021-01-01	null	null	null	iclr-2021-1	['smac-1', 'smac']	['playing-games', 'playing-games']	[-1.34242907e-01 1.62419468e-01 -1.54671654e-01 1.44339323e-01 -3.63652349e-01 -4.54277217e-01 6.58258677e-01 6.70209620e-03 -8.85684133e-01 8.56861353e-01 -3.26317586e-02 -2.58946776e-01 -3.48509192e-01 -6.39822066e-01 -6.90965354e-01 -7.65508413e-01 5.74603900e-02 7.88868368e-01 4.61977243e-01 -6.10648036e-01 1.24518849e-01 4.05396372e-02 -1.52934992e+00 1.33277178e-01 1.13427508e+00 6.41987085e-01 8.94311428e-01 5.05435288e-01 -3.24063189e-02 9.21391308e-01 -7.83137619e-01 -2.98039168e-01 3.29434663e-01 -5.20153642e-01 -7.68167973e-01 -3.89246806e-03 -1.46004066e-01 -5.81487417e-01 -1.78856462e-01 7.73360789e-01 6.73628271e-01 4.31979120e-01 4.83930618e-01 -1.44392872e+00 -8.72316718e-01 7.55673110e-01 -6.58358872e-01 1.74056619e-01 -1.88399758e-02 3.83383870e-01 1.08556962e+00 -3.01833719e-01 2.85496682e-01 1.19120395e+00 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d2d1fe6f-6aaf-49cd-afb5-3a344ad660fc	te-yolof-tiny-and-efficient-yolof-for-blood	2108.12313	null	https://arxiv.org/abs/2108.12313v1	https://arxiv.org/pdf/2108.12313v1.pdf	TE-YOLOF: Tiny and efficient YOLOF for blood cell detection	Blood cell detection in microscopic images is an essential branch of medical image processing research. Since disease detection based on manual checking of blood cells is time-consuming and full of errors, testing of blood cells using object detectors with Deep Convolutional Neural Network can be regarded as a feasible solution. In this work, an object detector based on YOLOF has been proposed to detect blood cell objects such as red blood cells, white blood cells and platelets. This object detector is called TE-YOLOF, Tiny and Efficient YOLOF, and it is a One-Stage detector using dilated encoder to extract information from single-level feature maps. For increasing efficiency and flexibility, the EfficientNet Convolutional Neural Network is utilized as the backbone for the proposed object detector. Furthermore, the Depthwise Separable Convolution is applied to enhance the performance and minimize the parameters of the network. In addition, the Mish activation function is employed to increase the precision. Extensive experiments on the BCCD dataset prove the effectiveness of the proposed model, which is more efficient than other existing studies for blood cell detection.	['Wei Xiang', 'Yali Wang', 'Hang Yang', 'Xiangkui Li', 'Fanxin Xu']	2021-08-27	null	null	null	null	['cell-detection', 'blood-cell-detection']	['computer-vision', 'medical']	[-3.60295385e-01 -3.38563353e-01 6.67543262e-02 -7.24265799e-02 2.29182959e-01 7.47607276e-02 8.19996893e-02 2.62456626e-01 -7.81082869e-01 5.02473891e-01 -1.48362190e-01 -3.87757085e-02 2.84167945e-01 -1.01530659e+00 -1.26945123e-01 -9.78206456e-01 1.50794417e-01 9.13021937e-02 5.45365870e-01 1.83648184e-01 2.08167002e-01 9.38409865e-01 -1.05858207e+00 1.89048678e-01 6.72015309e-01 1.24856114e+00 4.86040115e-02 6.94325268e-01 -3.84643346e-01 1.11568916e+00 -6.96145594e-01 -3.05305690e-01 8.95717293e-02 -3.75469416e-01 -2.77978778e-01 2.57727295e-01 -2.91784614e-01 -8.10893238e-01 -4.09655124e-01 1.05738044e+00 8.88087094e-01 -3.41039926e-01 6.37668014e-01 -8.69457781e-01 -4.68517721e-01 1.78796381e-01 -6.48505688e-01 8.07241857e-01 -7.48663917e-02 3.07435423e-01 8.47153738e-02 -7.55743504e-01 2.08949327e-01 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cee113b7-dc02-438f-9197-c38b377b7342	unleash-the-potential-of-3d-point-cloud	2306.00552	null	https://arxiv.org/abs/2306.00552v1	https://arxiv.org/pdf/2306.00552v1.pdf	Unleash the Potential of 3D Point Cloud Modeling with A Calibrated Local Geometry-driven Distance Metric	Quantifying the dissimilarity between two unstructured 3D point clouds is a challenging task, with existing metrics often relying on measuring the distance between corresponding points that can be either inefficient or ineffective. In this paper, we propose a novel distance metric called Calibrated Local Geometry Distance (CLGD), which computes the difference between the underlying 3D surfaces calibrated and induced by a set of reference points. By associating each reference point with two given point clouds through computing its directional distances to them, the difference in directional distances of an identical reference point characterizes the geometric difference between a typical local region of the two point clouds. Finally, CLGD is obtained by averaging the directional distance differences of all reference points. We evaluate CLGD on various optimization and unsupervised learning-based tasks, including shape reconstruction, rigid registration, scene flow estimation, and feature representation. Extensive experiments show that CLGD achieves significantly higher accuracy under all tasks in a memory and computationally efficient manner, compared with existing metrics. As a generic metric, CLGD has the potential to advance 3D point cloud modeling. The source code is publicly available at https://github.com/rsy6318/CLGD.	['Junhui Hou', 'Siyu Ren']	2023-06-01	null	null	null	null	['scene-flow-estimation']	['computer-vision']	[-1.52824089e-01 -5.52357197e-01 -3.67733352e-02 -1.95777193e-01 -8.31810653e-01 -5.93693018e-01 6.91360474e-01 4.95929569e-01 -1.74249977e-01 2.09231615e-01 2.47220173e-02 -5.24173826e-02 -1.26634285e-01 -8.35723758e-01 -4.76385236e-01 -5.05876422e-01 1.45202219e-01 6.22114837e-01 1.40979737e-01 7.48032853e-02 5.67678034e-01 1.05535483e+00 -1.42298865e+00 -2.99088687e-01 8.94818425e-01 1.00787282e+00 2.00499550e-01 4.46992397e-01 -2.48263016e-01 -2.66948268e-02 -2.37560198e-01 -4.87760454e-03 4.60647911e-01 -6.17693141e-02 -7.24317491e-01 -7.07703531e-02 6.19779527e-01 -3.69301662e-02 -1.46928743e-01 1.00668347e+00 4.03633028e-01 2.61449546e-01 6.26841903e-01 -1.32717741e+00 -4.64034915e-01 -1.89747974e-01 -5.92199564e-01 9.61646512e-02 5.66674709e-01 9.75592900e-03 8.86630177e-01 -1.27266443e+00 5.98976135e-01 1.12597477e+00 7.26157367e-01 2.16817915e-01 -1.36385298e+00 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0a66b1eb-18c7-4cd6-95e0-21fdfb9f4173	inspecting-spoken-language-understanding-from	2306.00482	null	https://arxiv.org/abs/2306.00482v1	https://arxiv.org/pdf/2306.00482v1.pdf	Inspecting Spoken Language Understanding from Kids for Basic Math Learning at Home	Enriching the quality of early childhood education with interactive math learning at home systems, empowered by recent advances in conversational AI technologies, is slowly becoming a reality. With this motivation, we implement a multimodal dialogue system to support play-based learning experiences at home, guiding kids to master basic math concepts. This work explores Spoken Language Understanding (SLU) pipeline within a task-oriented dialogue system developed for Kid Space, with cascading Automatic Speech Recognition (ASR) and Natural Language Understanding (NLU) components evaluated on our home deployment data with kids going through gamified math learning activities. We validate the advantages of a multi-task architecture for NLU and experiment with a diverse set of pretrained language representations for Intent Recognition and Entity Extraction tasks in the math learning domain. To recognize kids' speech in realistic home environments, we investigate several ASR systems, including the commercial Google Cloud and the latest open-source Whisper solutions with varying model sizes. We evaluate the SLU pipeline by testing our best-performing NLU models on noisy ASR output to inspect the challenges of understanding children for math learning in authentic homes.	['Lama Nachman', 'Saurav Sahay', 'Roddy Fuentes Alba', 'Eda Okur']	2023-06-01	null	null	null	null	['intent-recognition', 'spoken-language-understanding', 'spoken-language-understanding', 'automatic-speech-recognition']	['natural-language-processing', 'natural-language-processing', 'speech', 'speech']	[-2.04298645e-02 3.56235564e-01 3.03736061e-01 -6.25088751e-01 -8.48617733e-01 -6.67779028e-01 4.69761461e-01 1.78436458e-01 -2.04928711e-01 2.76914299e-01 8.98483217e-01 -5.15801609e-01 1.73915431e-01 -9.28380370e-01 -3.96457464e-01 3.58639732e-02 2.04464287e-01 6.37924135e-01 3.30071926e-01 -9.09637213e-01 -2.20908627e-01 -5.97024485e-02 -1.85027933e+00 9.43237066e-01 8.87272418e-01 4.01803792e-01 1.05898969e-01 1.43878889e+00 -4.82118130e-01 1.85368621e+00 -8.96402597e-01 -3.77900362e-01 -1.56880721e-01 -1.11051798e-01 -1.33535945e+00 -3.87421191e-01 5.21604836e-01 -8.58727992e-01 -4.24731016e-01 2.90651768e-01 8.42766881e-01 6.55889571e-01 1.64053202e-01 -1.18710244e+00 -8.27901959e-01 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a8ee294f-4e5c-4c92-9724-fdffcf9c53ab	tmu-nmt-system-with-automatic-post-editing-by	null	null	https://aclanthology.org/2022.wat-1.4	https://aclanthology.org/2022.wat-1.4.pdf	TMU NMT System with Automatic Post-Editing by Multi-Source Levenshtein Transformer for the Restricted Translation Task of WAT 2022	In this paper, we describe our TMU English–Japanese systems submitted to the restricted translation task at WAT 2022 (Nakazawa et al., 2022). In this task, we translate an input sentence with the constraint that certain words or phrases (called restricted target vocabularies (RTVs)) should be contained in the output sentence. To satisfy this constraint, we address this task using a combination of two techniques. One is lexical-constraint-aware neural machine translation (LeCA) (Chen et al., 2020), which is a method of adding RTVs at the end of input sentences. The other is multi-source Levenshtein transformer (MSLevT) (Wan et al., 2020), which is a non-autoregressive method for automatic post-editing. Our system generates translations in two steps. First, we generate the translation using LeCA. Subsequently, we filter the sentences that do not satisfy the constraints and post-edit them with MSLevT. Our experimental results reveal that 100% of the RTVs can be included in the generated sentences while maintaining the translation quality of the LeCA model on both English to Japanese (En→Ja) and Japanese to English (Ja→En) tasks. Furthermore, the method used in previous studies requires an increase in the beam size to satisfy the constraints, which is computationally expensive. In contrast, the proposed method does not require a similar increase and can generate translations faster.	['Mamoru Komachi', 'Seiichiro Kondo']	null	null	null	null	wat-2022-10	['automatic-post-editing', 'automatic-post-editing']	['computer-vision', 'natural-language-processing']	[ 5.61282337e-01 4.26702723e-02 9.07851458e-02 -3.71910363e-01 -1.07924771e+00 -6.40794635e-01 5.13596892e-01 -4.34788585e-01 -4.62662905e-01 1.15061569e+00 3.29140455e-01 -6.68833435e-01 3.60151023e-01 -6.91861153e-01 -7.91705251e-01 -4.25244659e-01 6.42012715e-01 4.83752519e-01 1.33032337e-01 -4.67443943e-01 -4.71953116e-02 1.50956422e-01 -9.90072131e-01 4.77557808e-01 1.20926440e+00 4.81765181e-01 6.72532797e-01 5.82011104e-01 -1.30223006e-01 3.01293522e-01 -5.64387441e-01 -5.60744107e-01 3.82830441e-01 -9.57161248e-01 -8.52348983e-01 -2.54490405e-01 1.75674111e-01 -1.07721716e-01 7.75019974e-02 1.17608404e+00 4.82037574e-01 4.97961156e-02 6.62768364e-01 -7.19274163e-01 -9.31207895e-01 1.04220951e+00 -3.42136979e-01 1.97337791e-01 2.93432653e-01 -7.36855045e-02 9.98695850e-01 -1.37986076e+00 7.04677105e-01 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38e427b4-5145-43a0-890e-9023c304f2eb	rigidity-strengthening-is-a-vital-mechanism	1704.05883	null	http://arxiv.org/abs/1704.05883v1	http://arxiv.org/pdf/1704.05883v1.pdf	Rigidity strengthening is a vital mechanism for protein-ligand binding	Protein-ligand binding is essential to almost all life processes. The understanding of protein-ligand interactions is fundamentally important to rational drug design and protein design. Based on large scale data sets, we show that protein rigidity strengthening or flexibility reduction is a pivoting mechanism in protein-ligand binding. Our approach based solely on rigidity is able to unveil a surprisingly long range contribution of four residue layers to protein-ligand binding, which has a ramification for drug and protein design. Additionally, the present work reveals that among various pairwise interactions, the short range ones within the distance of the van der Waals diameter are most important. It is found that the present approach outperforms all the other state-of-the-art scoring functions for protein-ligand binding affinity predictions of two benchmark data sets	[]	2017-03-31	null	null	null	null	['protein-design']	['medical']	[ 1.98635742e-01 -1.12959124e-01 -5.46175420e-01 -3.68692040e-01 -2.94956356e-01 -5.47546148e-01 2.73823947e-01 2.53144175e-01 -4.06955481e-01 1.34033239e+00 2.47396931e-01 -5.66155374e-01 -3.66529971e-01 -5.32307804e-01 -8.29543889e-01 -1.34190965e+00 -8.68668482e-02 6.85826302e-01 5.04996419e-01 -6.48911119e-01 4.47231293e-01 9.72546160e-01 -9.66437638e-01 2.53672421e-01 7.34904706e-01 3.29901069e-01 2.58359551e-01 2.46926650e-01 4.29405496e-02 2.17007160e-01 1.60412505e-01 -2.63543755e-01 -1.43735096e-01 -2.70154208e-01 -8.82209480e-01 -5.77384233e-01 -1.52702674e-01 3.66154790e-01 4.96949069e-02 4.44302619e-01 8.27557445e-01 1.00393839e-01 9.88376677e-01 -6.01640530e-02 -5.99090755e-01 2.28509873e-01 -4.69815314e-01 2.33932018e-01 5.85215807e-01 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6f1620a6-9ed8-4b36-abf7-abaa4562b1d4	factify3m-a-benchmark-for-multimodal-fact	2306.05523	null	https://arxiv.org/abs/2306.05523v1	https://arxiv.org/pdf/2306.05523v1.pdf	FACTIFY3M: A Benchmark for Multimodal Fact Verification with Explainability through 5W Question-Answering	Combating disinformation is one of the burning societal crises -- about 67% of the American population believes that disinformation produces a lot of uncertainty, and 10% of them knowingly propagate disinformation. Evidence shows that disinformation can manipulate democratic processes and public opinion, causing disruption in the share market, panic and anxiety in society, and even death during crises. Therefore, disinformation should be identified promptly and, if possible, mitigated. With approximately 3.2 billion images and 720,000 hours of video shared online daily on social media platforms, scalable detection of multimodal disinformation requires efficient fact verification. Despite progress in automatic text-based fact verification (e.g., FEVER, LIAR), the research community lacks substantial effort in multimodal fact verification. To address this gap, we introduce FACTIFY 3M, a dataset of 3 million samples that pushes the boundaries of the domain of fact verification via a multimodal fake news dataset, in addition to offering explainability through the concept of 5W question-answering. Salient features of the dataset include: (i) textual claims, (ii) ChatGPT-generated paraphrased claims, (iii) associated images, (iv) stable diffusion-generated additional images (i.e., visual paraphrases), (v) pixel-level image heatmap to foster image-text explainability of the claim, (vi) 5W QA pairs, and (vii) adversarial fake news stories.	['Amitava Das', 'Amit Sheth', 'Aman Chadha', 'Parth Patwa', 'Shreyash Mishra', 'Suryavardan S', 'Dwip Dalal', 'Kinjal Sensharma', 'Shreyas Chatterjee', 'Samahriti Mukherjee', 'Ritvik G', 'Preethi Gurumurthy', 'Janvita Reddy', 'Ishan Paul', 'Harshit Dave', 'Arghya Sarkar', 'Aditya Pakala', 'Adarsh Mahor', 'Anku Rani', 'Khusbu Pahwa', 'Megha Chakraborty']	2023-05-22	null	null	null	null	['fact-verification']	['natural-language-processing']	[ 3.44797254e-01 4.77557391e-01 -4.29969698e-01 -7.52420202e-02 -1.09581268e+00 -1.04636300e+00 9.65257585e-01 4.14302826e-01 2.87054088e-02 6.49892747e-01 9.05814111e-01 -6.56451941e-01 3.19214433e-01 -6.63577557e-01 -7.09059596e-01 -1.94654614e-01 3.55926603e-01 1.43402755e-01 -5.88309020e-02 -5.67276180e-01 6.10530138e-01 5.87710813e-02 -7.58387923e-01 8.10113311e-01 9.02032316e-01 9.47021365e-01 -5.91941833e-01 5.33874750e-01 -1.50689006e-01 1.39908218e+00 -8.48878384e-01 -1.32896030e+00 1.16914652e-01 -6.80492520e-01 -7.69690275e-01 3.29428554e-01 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007956d2-8408-414f-bacb-43a1416675f6	probabilistic-time-series-forecasting-with-1	null	null	http://proceedings.neurips.cc/paper/2020/hash/2f2b265625d76a6704b08093c652fd79-Abstract.html	http://proceedings.neurips.cc/paper/2020/file/2f2b265625d76a6704b08093c652fd79-Paper.pdf	Probabilistic Time Series Forecasting with Shape and Temporal Diversity	Probabilistic forecasting consists in predicting a distribution of possible future outcomes. In this paper, we address this problem for non-stationary time series, which is very challenging yet crucially important. We introduce the STRIPE model for representing structured diversity based on shape and time features, ensuring both probable predictions while being sharp and accurate. STRIPE is agnostic to the forecasting model, and we equip it with a diversification mechanism relying on determinantal point processes (DPP). We introduce two DPP kernels for modelling diverse trajectories in terms of shape and time, which are both differentiable and proved to be positive semi-definite. To have an explicit control on the diversity structure, we also design an iterative sampling mechanism to disentangle shape and time representations in the latent space. Experiments carried out on synthetic datasets show that STRIPE significantly outperforms baseline methods for representing diversity, while maintaining accuracy of the forecasting model. We also highlight the relevance of the iterative sampling scheme and the importance to use different criteria for measuring quality and diversity. Finally, experiments on real datasets illustrate that STRIPE is able to outperform state-of-the-art probabilistic forecasting approaches in the best sample prediction.	['Nicolas Thome', 'Vincent Le Guen']	2020-12-01	null	null	null	neurips-2020-12	['probabilistic-time-series-forecasting']	['time-series']	[-1.17402412e-01 -1.99152544e-01 -1.02071129e-01 -2.78408796e-01 -7.21594274e-01 -7.87662923e-01 1.23916423e+00 2.02439595e-02 1.84475631e-01 7.30355740e-01 3.68551582e-01 -2.85354167e-01 -5.88702381e-01 -7.63026595e-01 -5.41507363e-01 -1.00799012e+00 -4.33691829e-01 5.96590400e-01 2.26862967e-01 -1.55787632e-01 5.00244558e-01 6.42623901e-01 -1.79285097e+00 1.35384649e-01 9.86483395e-01 1.11299217e+00 -1.17114551e-01 6.51913226e-01 6.51348159e-02 7.69262612e-01 -3.59153569e-01 -4.95312870e-01 1.40582249e-01 -1.99544966e-01 -3.54790181e-01 4.14487235e-02 4.28251028e-02 2.35440329e-01 1.82336777e-01 5.56587756e-01 2.70031452e-01 -6.22514226e-02 1.38393879e+00 -1.44309556e+00 -4.39606845e-01 3.44354510e-01 -1.94284618e-01 1.78117961e-01 2.11061686e-01 -7.92284682e-03 1.04741108e+00 -8.50094676e-01 4.12114650e-01 1.06716192e+00 1.09638524e+00 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a9c1d0f0-4595-4848-a602-f1a6b4375cd4	a-database-for-face-presentation-attack-using	1906.11900	null	https://arxiv.org/abs/1906.11900v1	https://arxiv.org/pdf/1906.11900v1.pdf	A database for face presentation attack using wax figure faces	Compared to 2D face presentation attacks (e.g. printed photos and video replays), 3D type attacks are more challenging to face recognition systems (FRS) by presenting 3D characteristics or materials similar to real faces. Existing 3D face spoofing databases, however, mostly based on 3D masks, are restricted to small data size or poor authenticity due to the production difficulty and high cost. In this work, we introduce the first wax figure face database, WFFD, as one type of super-realistic 3D presentation attacks to spoof the FRS. This database consists of 2200 images with both real and wax figure faces (totally 4400 faces) with a high diversity from online collections. Experiments on this database first investigate the vulnerability of three popular FRS to this kind of new attack. Further, we evaluate the performance of several face presentation attack detection methods to show the attack abilities of this super-realistic face spoofing database.	['Zhengquan Xu', 'Guodong Guo', 'Shan Jia', 'Chuanbo Hu']	2019-06-06	null	null	null	null	['face-presentation-attack-detection', 'small-data']	['computer-vision', 'computer-vision']	[ 2.50297546e-01 -2.26316735e-01 1.31449163e-01 -1.17463671e-01 -1.64686099e-01 -8.27202141e-01 7.99711108e-01 -4.91312742e-01 1.31475747e-01 3.74953568e-01 -9.53007787e-02 -5.08086562e-01 -3.86569910e-02 -6.59333050e-01 -5.62898755e-01 -6.75498784e-01 -6.46034300e-01 -6.27875626e-02 2.35008821e-01 -3.46965313e-01 2.62968034e-01 1.23733139e+00 -1.74148154e+00 2.65912116e-01 1.99543595e-01 8.92597497e-01 -2.83629179e-01 5.33699453e-01 4.87108119e-02 -1.75293982e-01 -1.34156120e+00 -9.67698514e-01 6.03367150e-01 -1.58188134e-01 -2.84863532e-01 1.22205056e-01 7.16682315e-01 -6.76441789e-01 -6.05550945e-01 1.17276955e+00 7.26526558e-01 -4.87495273e-01 5.39077044e-01 -1.71045959e+00 -8.37498844e-01 1.40649125e-01 -7.50196993e-01 3.62741679e-01 1.09332013e+00 1.33573025e-01 -1.67652339e-01 -8.45106423e-01 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4950f89e-6ae9-4f36-8d43-fa562df09307	explanation-based-handwriting-verification	1909.02548	null	https://arxiv.org/abs/1909.02548v1	https://arxiv.org/pdf/1909.02548v1.pdf	Explanation based Handwriting Verification	Deep learning system have drawback that their output is not accompanied with ex-planation. In a domain such as forensic handwriting verification it is essential to provideexplanation to jurors. The goal of handwriting verification is to find a measure of confi-dence whether the given handwritten samples are written by the same or different writer.We propose a method to generate explanations for the confidence provided by convolu-tional neural network (CNN) which maps the input image to 15 annotations (features)provided by experts. Our system comprises of: (1) Feature learning network (FLN),a differentiable system, (2) Inference module for providing explanations. Furthermore,inference module provides two types of explanations: (a) Based on cosine similaritybetween categorical probabilities of each feature, (b) Based on Log-Likelihood Ratio(LLR) using directed probabilistic graphical model. We perform experiments using acombination of feature learning network (FLN) and each inference module. We evaluateour system using XAI-AND dataset, containing 13700 handwritten samples and 15 cor-responding expert examined features for each sample. The dataset is released for publicuse and the methods can be extended to provide explanations on other verification taskslike face verification and bio-medical comparison. This dataset can serve as the basis and benchmark for future research in explanation based handwriting verification. The code is available on github.	['Mohammad Abuzar Shaikh', 'Mihir Chauhan', 'Sargur N. Srihari']	2019-08-14	null	null	null	null	['handwriting-verification']	['computer-vision']	[ 3.80313396e-02 3.42688352e-01 -5.45810342e-01 -9.40360367e-01 -6.88700259e-01 -6.09144807e-01 7.30326355e-01 5.11853881e-02 -1.02141194e-01 9.35576320e-01 2.03296885e-01 -7.17629194e-01 -2.66981423e-01 -6.60022080e-01 -6.58798754e-01 -6.83472216e-01 2.46368408e-01 5.69670677e-01 -2.47365966e-01 2.91278630e-01 6.41738534e-01 1.04855692e+00 -1.19402671e+00 6.56694353e-01 6.96268737e-01 9.59375978e-01 -4.10106778e-01 6.83589399e-01 -2.02773362e-02 7.61501849e-01 -7.57443845e-01 -8.49803865e-01 -5.81052303e-02 -5.51833391e-01 -7.82810569e-01 2.82152444e-01 4.94267851e-01 -3.58410001e-01 -3.49437445e-01 1.09682894e+00 3.36204767e-01 -2.98174210e-02 1.04470563e+00 -1.53207946e+00 -1.15863431e+00 7.03126490e-01 -2.57379144e-01 5.60349375e-02 3.44305277e-01 2.06305146e-01 7.19336987e-01 -1.04491949e+00 5.45963526e-01 1.27556777e+00 4.21668500e-01 7.66163349e-01 -8.13112915e-01 -8.68368983e-01 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1.83750212e-01 -5.15118778e-01 -2.04669327e-01 1.40840068e-01]	[11.630926132202148, 2.2039272785186768]
1207a8af-23e6-4f8e-903a-0713b027c318	endowing-robots-with-longer-term-autonomy-by	1809.03979	null	http://arxiv.org/abs/1809.03979v1	http://arxiv.org/pdf/1809.03979v1.pdf	Endowing Robots with Longer-term Autonomy by Recovering from External Disturbances in Manipulation through Grounded Anomaly Classification and Recovery Policies	Robot manipulation is increasingly poised to interact with humans in co-shared workspaces. Despite increasingly robust manipulation and control algorithms, failure modes continue to exist whenever models do not capture the dynamics of the unstructured environment. To obtain longer-term horizons in robot automation, robots must develop introspection and recovery abilities. We contribute a set of recovery policies to deal with anomalies produced by external disturbances as well as anomaly classification through the use of non-parametric statistics with memoized variational inference with scalable adaptation. A recovery critic stands atop of a tightly-integrated, graph-based online motion-generation and introspection system that resolves a wide range of anomalous situations. Policies, skills, and introspection models are learned incrementally and contextually in a task. Two task-level recovery policies: re-enactment and adaptation resolve accidental and persistent anomalies respectively. The introspection system uses non-parametric priors along with Markov jump linear systems and memoized variational inference with scalable adaptation to learn a model from the data. Extensive real-robot experimentation with various strenuous anomalous conditions is induced and resolved at different phases of a task and in different combinations. The system executes around-the-clock introspection and recovery and even elicited self-recovery when misclassifications occurred.	['Sakmongkon Chumkamon', 'Longxin Chen', 'Shuangqi Luo', 'Shuangda Duan', 'Yisheng Guan', 'Hongmin Wu', 'Juan Rojas', 'Dong Liu']	2018-09-11	null	null	null	null	['anomaly-classification']	['computer-vision']	[ 1.92409307e-01 2.80481994e-01 1.13254912e-01 -1.62062924e-02 -2.74299055e-01 -2.31883958e-01 4.38990533e-01 1.04002528e-01 -3.27801228e-01 8.27280939e-01 -1.36077687e-01 -1.06227115e-01 -6.08174801e-01 -1.71571955e-01 -7.08875835e-01 -4.76360828e-01 -5.59794366e-01 1.02146244e+00 4.53695416e-01 -3.50980133e-01 4.96978283e-01 6.95947051e-01 -1.62547827e+00 -1.45722792e-01 9.26151574e-01 4.67712700e-01 4.81285006e-01 1.23806834e+00 4.03343737e-01 9.59385872e-01 -5.43519318e-01 5.66407919e-01 5.75313628e-01 -1.37706861e-01 -5.94148695e-01 4.24968779e-01 -8.33652392e-02 -1.85700968e-01 -4.03216630e-01 9.65918660e-01 2.53155202e-01 7.29870796e-01 6.47781491e-01 -1.69723558e+00 -3.46470743e-01 2.53959835e-01 -4.08591390e-01 3.05348873e-01 4.21380997e-01 1.01382947e+00 1.24250650e-01 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c468d417-0b74-4d29-bd1f-f46af94b85f9	sts-uhh-at-semeval-2017-task-1-scoring	null	null	https://aclanthology.org/S17-2025	https://aclanthology.org/S17-2025.pdf	STS-UHH at SemEval-2017 Task 1: Scoring Semantic Textual Similarity Using Supervised and Unsupervised Ensemble	This paper reports the STS-UHH participation in the SemEval 2017 shared Task 1 of Semantic Textual Similarity (STS). Overall, we submitted 3 runs covering monolingual and cross-lingual STS tracks. Our participation involves two approaches: unsupervised approach, which estimates a word alignment-based similarity score, and supervised approach, which combines dependency graph similarity and coverage features with lexical similarity measures using regression methods. We also present a way on ensembling both models. Out of 84 submitted runs, our team best multi-lingual run has been ranked 12th in overall performance with correlation of 0.61, 7th among 31 participating teams.	['Chris Biemann', 'Sarah Kohail', 'Amr Rekaby Salama']	2017-08-01	null	null	null	semeval-2017-8	['graph-similarity']	['graphs']	[-1.66281238e-01 2.51619041e-01 -3.46661806e-01 -3.43704224e-01 -1.35384297e+00 -6.38465881e-01 7.28186250e-01 7.48846889e-01 -6.81626916e-01 5.37030041e-01 9.04479980e-01 -4.09613885e-02 -2.01354362e-02 -1.55269206e-01 -4.19004023e-01 -1.52459340e-02 9.75685269e-02 7.66607404e-01 2.85683751e-01 -5.64630210e-01 6.06993854e-01 -2.81294018e-01 -1.16105545e+00 5.75049579e-01 9.70661342e-01 7.32058287e-01 1.48493379e-01 4.20895994e-01 -3.28759640e-01 7.92732358e-01 -2.05749750e-01 -8.69169295e-01 -1.28598526e-01 -5.50316572e-01 -1.42726970e+00 -7.14618385e-01 6.28752947e-01 7.82224178e-01 -1.60104588e-01 7.83446550e-01 5.76132059e-01 9.45059806e-02 7.31196046e-01 -1.01750124e+00 -5.88505864e-01 1.39292073e+00 -2.78758734e-01 5.60988665e-01 9.28861320e-01 -3.43588978e-01 1.73495436e+00 -1.08188283e+00 1.13832128e+00 1.16926622e+00 1.10961723e+00 4.57830727e-02 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579f4c9d-e896-4169-b8f9-fc237b0dc1fc	compound-multi-branch-feature-fusion-for-real-1	2206.02748	null	https://arxiv.org/abs/2206.02748v1	https://arxiv.org/pdf/2206.02748v1.pdf	Compound Multi-branch Feature Fusion for Real Image Restoration	Image restoration is a challenging and ill-posed problem which also has been a long-standing issue. However, most of learning based restoration methods are proposed to target one degradation type which means they are lack of generalization. In this paper, we proposed a multi-branch restoration model inspired from the Human Visual System (i.e., Retinal Ganglion Cells) which can achieve multiple restoration tasks in a general framework. The experiments show that the proposed multi-branch architecture, called CMFNet, has competitive performance results on four datasets, including image dehazing, deraindrop, and deblurring, which are very common applications for autonomous cars. The source code and pretrained models of three restoration tasks are available at https://github.com/FanChiMao/CMFNet.	['Kuan-Hsien Liu', 'Tsung-Jung Liu', 'Chi-Mao Fan']	2022-06-02	compound-multi-branch-feature-fusion-for-real	https://openreview.net/forum?id=WQIdU90Gsu	https://openreview.net/pdf?id=WQIdU90Gsu	null	['image-dehazing']	['computer-vision']	[ 9.55607221e-02 -3.62559944e-01 4.78391722e-02 -1.16267866e-02 -4.61395085e-01 9.63849872e-02 5.63189089e-01 -4.92665589e-01 -1.55700982e-01 7.02725291e-01 3.34073365e-01 -1.47677287e-01 1.51811376e-01 -6.94823742e-01 -7.48199582e-01 -1.05467236e+00 5.67005038e-01 -2.77257949e-01 3.94054770e-01 -4.16548312e-01 4.13975000e-01 2.13371575e-01 -1.65618598e+00 3.17478031e-01 1.12695217e+00 8.99127662e-01 2.85689145e-01 4.50906992e-01 1.92619860e-01 8.03984463e-01 -5.25985420e-01 -2.38663271e-01 8.19396377e-02 -4.68958288e-01 -4.21151966e-01 2.23951727e-01 5.01405895e-01 -5.20683408e-01 -8.03772688e-01 1.41589737e+00 4.41232800e-01 9.94769856e-02 7.03505456e-01 -1.28724122e+00 -1.26947188e+00 -5.36151901e-02 -5.67234516e-01 5.12139380e-01 -5.91840222e-02 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8f56e30b-87e4-44e2-a812-c6ec8ed70437	vision-through-the-veil-differential-privacy	2306.17794	null	https://arxiv.org/abs/2306.17794v1	https://arxiv.org/pdf/2306.17794v1.pdf	Vision Through the Veil: Differential Privacy in Federated Learning for Medical Image Classification	The proliferation of deep learning applications in healthcare calls for data aggregation across various institutions, a practice often associated with significant privacy concerns. This concern intensifies in medical image analysis, where privacy-preserving mechanisms are paramount due to the data being sensitive in nature. Federated learning, which enables cooperative model training without direct data exchange, presents a promising solution. Nevertheless, the inherent vulnerabilities of federated learning necessitate further privacy safeguards. This study addresses this need by integrating differential privacy, a leading privacy-preserving technique, into a federated learning framework for medical image classification. We introduce a novel differentially private federated learning model and meticulously examine its impacts on privacy preservation and model performance. Our research confirms the existence of a trade-off between model accuracy and privacy settings. However, we demonstrate that strategic calibration of the privacy budget in differential privacy can uphold robust image classification performance while providing substantial privacy protection.	['Balasubramanian Raman', 'Uppala Vivek Narayan', 'Pradeep Singh', 'Kishore Babu Nampalle']	2023-06-30	null	null	null	null	['medical-image-classification']	['medical']	[ 4.28582020e-02 2.54702389e-01 -2.24186808e-01 -5.35204709e-01 -8.92233670e-01 -8.38557422e-01 2.21234202e-01 4.95972425e-01 -6.09512925e-01 6.42293990e-01 1.94773719e-01 -7.72738576e-01 -3.40819627e-01 -7.36366451e-01 -5.10745168e-01 -9.61708069e-01 -2.12983176e-01 -3.08455765e-01 -6.06671453e-01 3.95966738e-01 -1.10280454e-01 7.31989384e-01 -1.09460723e+00 3.94308418e-01 8.65767777e-01 1.04724216e+00 -4.82408524e-01 3.47801000e-01 -6.21071644e-02 8.21635067e-01 -4.29080784e-01 -1.08739316e+00 8.44283581e-01 -1.58145070e-01 -7.82485902e-01 -2.05167398e-01 3.11298132e-01 -6.30393088e-01 -4.61855590e-01 1.13961935e+00 4.60576445e-01 -3.40322942e-01 -1.66456942e-02 -1.55777347e+00 -4.25183684e-01 4.03590202e-01 -2.96222359e-01 3.88587415e-02 -1.87511116e-01 1.42009616e-01 9.61803377e-01 -1.12152971e-01 5.40990353e-01 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75ab4984-3701-49f3-899a-de0b9feb25c9	skill-critic-refining-learned-skills-for	2306.08388	null	https://arxiv.org/abs/2306.08388v2	https://arxiv.org/pdf/2306.08388v2.pdf	Skill-Critic: Refining Learned Skills for Reinforcement Learning	Hierarchical reinforcement learning (RL) can accelerate long-horizon decision-making by temporally abstracting a policy into multiple levels. Promising results in sparse reward environments have been seen with skills, i.e. sequences of primitive actions. Typically, a skill latent space and policy are discovered from offline data, but the resulting low-level policy can be unreliable due to low-coverage demonstrations or distribution shifts. As a solution, we propose fine-tuning the low-level policy in conjunction with high-level skill selection. Our Skill-Critic algorithm optimizes both the low and high-level policies; these policies are also initialized and regularized by the latent space learned from offline demonstrations to guide the joint policy optimization. We validate our approach in multiple sparse RL environments, including a new sparse reward autonomous racing task in Gran Turismo Sport. The experiments show that Skill-Critic's low-level policy fine-tuning and demonstration-guided regularization are essential for optimal performance. Images and videos are available at https://sites.google.com/view/skill-critic. We plan to open source the code with the final version.	['Wei Zhan', 'Masayoshi Tomizuka', 'Kenta Kawamoto', 'Chen Tang', 'Catherine Weaver', 'Ce Hao']	2023-06-14	null	null	null	null	['hierarchical-reinforcement-learning']	['methodology']	[-3.20611931e-02 -2.48545483e-02 -4.79071647e-01 -2.07968682e-01 -9.02139604e-01 -4.68174517e-01 5.73257029e-01 -1.58495083e-01 -6.73612356e-01 1.07195795e+00 4.51840609e-01 -7.32212067e-02 -6.29694164e-02 -3.07898611e-01 -9.00638163e-01 -8.06569695e-01 -4.29342717e-01 5.22313952e-01 2.84846812e-01 -2.27803007e-01 3.03599596e-01 3.66146535e-01 -1.65449798e+00 1.49760067e-01 1.06020188e+00 5.63033938e-01 5.87967932e-01 8.36888552e-01 2.97921151e-01 1.16444671e+00 -4.95151520e-01 2.97836572e-01 5.86742282e-01 -5.42136550e-01 -5.18708348e-01 2.99045928e-02 2.10210010e-01 -6.33340657e-01 -4.11557972e-01 1.00153112e+00 3.47988516e-01 7.13213563e-01 3.05938423e-01 -1.27229857e+00 -2.66966790e-01 6.12908840e-01 -4.60607260e-01 1.95010707e-01 3.15502703e-01 9.02681828e-01 9.29530859e-01 -4.58255827e-01 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77de38be-5ba9-4e0e-99df-f592419ba16c	tyger-task-type-generic-active-learning-for	2205.11279	null	https://arxiv.org/abs/2205.11279v1	https://arxiv.org/pdf/2205.11279v1.pdf	Tyger: Task-Type-Generic Active Learning for Molecular Property Prediction	How to accurately predict the properties of molecules is an essential problem in AI-driven drug discovery, which generally requires a large amount of annotation for training deep learning models. Annotating molecules, however, is quite costly because it requires lab experiments conducted by experts. To reduce annotation cost, deep Active Learning (AL) methods are developed to select only the most representative and informative data for annotating. However, existing best deep AL methods are mostly developed for a single type of learning task (e.g., single-label classification), and hence may not perform well in molecular property prediction that involves various task types. In this paper, we propose a Task-type-generic active learning framework (termed Tyger) that is able to handle different types of learning tasks in a unified manner. The key is to learn a chemically-meaningful embedding space and perform active selection fully based on the embeddings, instead of relying on task-type-specific heuristics (e.g., class-wise prediction probability) as done in existing works. Specifically, for learning the embedding space, we instantiate a querying module that learns to translate molecule graphs into corresponding SMILES strings. Furthermore, to ensure that samples selected from the space are both representative and informative, we propose to shape the embedding space by two learning objectives, one based on domain knowledge and the other leveraging feedback from the task learner (i.e., model that performs the learning task at hand). We conduct extensive experiments on benchmark datasets of different task types. Experimental results show that Tyger consistently achieves high AL performance on molecular property prediction, outperforming baselines by a large margin. We also perform ablative experiments to verify the effectiveness of each component in Tyger.	['Xinchao Wang', 'Tingyang Xu', 'Jian Tang', 'Jiashi Feng', 'Kaixin Wang', 'Kuangqi Zhou']	2022-05-23	null	null	null	null	['molecular-property-prediction']	['miscellaneous']	[ 4.35953587e-01 -5.14848046e-02 -6.03118360e-01 -3.38656276e-01 -1.00831890e+00 -6.84688032e-01 2.98061192e-01 5.86078346e-01 -3.91027749e-01 9.69397545e-01 -1.39963076e-01 -3.05594891e-01 -1.09337382e-01 -8.67433548e-01 -8.99703443e-01 -1.02301729e+00 1.01446941e-01 6.49442196e-01 6.61040097e-02 1.75156236e-01 1.61810130e-01 4.60714012e-01 -1.04050565e+00 1.20667383e-01 1.41016006e+00 1.00303864e+00 2.88410723e-01 1.07535638e-01 -9.06942561e-02 6.23325109e-01 -4.33866799e-01 -4.06348258e-01 2.36342400e-02 -4.37896162e-01 -8.35612178e-01 -7.22462460e-02 9.57406387e-02 6.07490204e-02 1.50499985e-01 9.26943362e-01 6.31415963e-01 1.86423659e-01 8.26571763e-01 -1.00810409e+00 -4.52251405e-01 4.72524524e-01 -1.97597921e-01 -1.96118608e-01 2.43205830e-01 3.56806755e-01 1.25661826e+00 -9.86971259e-01 5.04290521e-01 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1d5475db-3242-40ba-a7c8-abb8ba30bf33	contrastive-code-representation-learning	2007.04973	null	https://arxiv.org/abs/2007.04973v4	https://arxiv.org/pdf/2007.04973v4.pdf	Contrastive Code Representation Learning	Recent work learns contextual representations of source code by reconstructing tokens from their context. For downstream semantic understanding tasks like summarizing code in English, these representations should ideally capture program functionality. However, we show that the popular reconstruction-based BERT model is sensitive to source code edits, even when the edits preserve semantics. We propose ContraCode: a contrastive pre-training task that learns code functionality, not form. ContraCode pre-trains a neural network to identify functionally similar variants of a program among many non-equivalent distractors. We scalably generate these variants using an automated source-to-source compiler as a form of data augmentation. Contrastive pre-training improves JavaScript summarization and TypeScript type inference accuracy by 2% to 13%. We also propose a new zero-shot JavaScript code clone detection dataset, showing that ContraCode is both more robust and semantically meaningful. On it, we outperform RoBERTa by 39% AUROC in an adversarial setting and up to 5% on natural code.	['Joseph E. Gonzalez', 'Ion Stoica', 'Ajay Jain', 'Pieter Abbeel', 'Paras Jain', 'Tianjun Zhang']	2020-07-09	null	https://aclanthology.org/2021.emnlp-main.482	https://aclanthology.org/2021.emnlp-main.482.pdf	emnlp-2021-11	['code-summarization', 'type-prediction', 'method-name-prediction']	['computer-code', 'computer-code', 'natural-language-processing']	[ 4.87905979e-01 3.68487924e-01 -4.75797802e-01 -4.26974714e-01 -1.21298444e+00 -1.00384653e+00 4.99404311e-01 5.35067856e-01 -8.51429403e-02 3.11230600e-01 5.08241773e-01 -6.53562784e-01 5.47930777e-01 -9.26949799e-01 -1.42624640e+00 -5.06736189e-02 -3.64696421e-02 -3.62186581e-02 1.70458958e-01 -1.59455627e-01 4.78072613e-01 -1.04143523e-01 -1.65722454e+00 8.41099858e-01 9.40725863e-01 3.13483506e-01 2.69451022e-01 1.11006832e+00 -4.18744683e-01 1.16593587e+00 -8.74980211e-01 -7.02511072e-01 6.75288737e-02 -4.76211160e-01 -1.16405857e+00 -5.81534445e-01 7.51103878e-01 -2.61199474e-01 -1.38264582e-01 1.16305304e+00 2.13811040e-01 -1.82372957e-01 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c4b2d7cc-b67c-44e1-bcd4-8944044a8166	open-vocabulary-panoptic-segmentation-with-2	2303.11324	null	https://arxiv.org/abs/2303.11324v1	https://arxiv.org/pdf/2303.11324v1.pdf	Open-vocabulary Panoptic Segmentation with Embedding Modulation	Open-vocabulary image segmentation is attracting increasing attention due to its critical applications in the real world. Traditional closed-vocabulary segmentation methods are not able to characterize novel objects, whereas several recent open-vocabulary attempts obtain unsatisfactory results, i.e., notable performance reduction on the closed vocabulary and massive demand for extra data. To this end, we propose OPSNet, an omnipotent and data-efficient framework for Open-vocabulary Panoptic Segmentation. Specifically, the exquisitely designed Embedding Modulation module, together with several meticulous components, enables adequate embedding enhancement and information exchange between the segmentation model and the visual-linguistic well-aligned CLIP encoder, resulting in superior segmentation performance under both open- and closed-vocabulary settings with much fewer need of additional data. Extensive experimental evaluations are conducted across multiple datasets (e.g., COCO, ADE20K, Cityscapes, and PascalContext) under various circumstances, where the proposed OPSNet achieves state-of-the-art results, which demonstrates the effectiveness and generality of the proposed approach. The code and trained models will be made publicly available.	['Hengshuang Zhao', 'Antonio Torralba', 'Ser-Nam Lim', 'Shuang Li', 'Xi Chen']	2023-03-20	null	null	null	null	['panoptic-segmentation', 'open-vocabulary-panoptic-segmentation']	['computer-vision', 'computer-vision']	[ 3.32136184e-01 7.67564168e-03 -2.18736783e-01 -1.76439837e-01 -8.83649290e-01 -5.37769258e-01 4.14847702e-01 -1.11725509e-01 -5.54125190e-01 5.46152294e-01 -5.39903715e-02 -6.22325838e-02 -3.21664242e-03 -5.05955637e-01 -5.29642582e-01 -7.00734079e-01 1.46283999e-01 3.19893897e-01 3.62082154e-01 -2.70079434e-01 8.09810460e-02 -1.45616978e-02 -1.60028005e+00 -3.86490434e-01 1.13622797e+00 1.17542624e+00 4.50380266e-01 4.36568081e-01 -1.39946729e-01 1.31737098e-01 -2.31688321e-01 -7.04572260e-01 3.27336699e-01 -7.43816942e-02 -7.19337881e-01 3.78635556e-01 5.33583641e-01 -8.96494836e-02 -3.65587175e-01 1.15686142e+00 5.16329169e-01 8.79037157e-02 5.09819806e-01 -8.33695889e-01 -7.48344064e-01 5.00431180e-01 -7.20788181e-01 2.67357498e-01 4.81941737e-03 2.25571200e-01 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1817b2ec-576e-4c29-a638-c0891ccb6552	simpleclick-interactive-image-segmentation	2210.11006	null	https://arxiv.org/abs/2210.11006v3	https://arxiv.org/pdf/2210.11006v3.pdf	SimpleClick: Interactive Image Segmentation with Simple Vision Transformers	Click-based interactive image segmentation aims at extracting objects with a limited user clicking. A hierarchical backbone is the de-facto architecture for current methods. Recently, the plain, non-hierarchical Vision Transformer (ViT) has emerged as a competitive backbone for dense prediction tasks. This design allows the original ViT to be a foundation model that can be finetuned for downstream tasks without redesigning a hierarchical backbone for pretraining. Although this design is simple and has been proven effective, it has not yet been explored for interactive image segmentation. To fill this gap, we propose SimpleClick, the first interactive segmentation method that leverages a plain backbone. Based on the plain backbone, we introduce a symmetric patch embedding layer that encodes clicks into the backbone with minor modifications to the backbone itself. With the plain backbone pretrained as a masked autoencoder (MAE), SimpleClick achieves state-of-the-art performance. Remarkably, our method achieves 4.15 NoC@90 on SBD, improving 21.8% over the previous best result. Extensive evaluation on medical images demonstrates the generalizability of our method. We further develop an extremely tiny ViT backbone for SimpleClick and provide a detailed computational analysis, highlighting its suitability as a practical annotation tool.	['Marc Niethammer', 'Gedas Bertasius', 'Zhenlin Xu', 'Qin Liu']	2022-10-20	null	null	null	null	['interactive-segmentation']	['computer-vision']	[ 3.65457535e-01 5.91842592e-01 -1.92250803e-01 -4.10984129e-01 -7.78306365e-01 -4.21974152e-01 1.64275438e-01 -1.69411376e-01 -4.77479607e-01 1.34219885e-01 1.36091754e-01 -3.98828208e-01 5.13016403e-01 -5.78294635e-01 -1.04486823e+00 -7.71538854e-01 1.67396933e-01 3.46394628e-01 8.06721866e-01 1.72362030e-02 6.51955511e-03 1.72164068e-01 -1.30009782e+00 5.62163353e-01 6.46730423e-01 1.07213485e+00 5.13328552e-01 5.25606036e-01 1.51302576e-01 6.09448195e-01 -4.71697897e-01 -5.40585816e-01 4.11743879e-01 -1.15912370e-01 -9.82462943e-01 -4.14841867e-04 7.03400612e-01 -7.63928473e-01 -3.84903103e-01 5.48481047e-01 5.21239042e-01 -2.45010138e-01 3.31820548e-01 -1.05199277e+00 -6.13640308e-01 9.16692078e-01 -5.80908835e-01 2.40295120e-02 -2.27002171e-03 3.16199154e-01 1.31907451e+00 -8.34519148e-01 6.89464331e-01 6.49820924e-01 1.08439207e+00 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d581db44-60d3-491b-80c1-26e5d422dbe1	intervention-generalization-a-view-from	2306.04027	null	https://arxiv.org/abs/2306.04027v1	https://arxiv.org/pdf/2306.04027v1.pdf	Intervention Generalization: A View from Factor Graph Models	One of the goals of causal inference is to generalize from past experiments and observational data to novel conditions. While it is in principle possible to eventually learn a mapping from a novel experimental condition to an outcome of interest, provided a sufficient variety of experiments is available in the training data, coping with a large combinatorial space of possible interventions is hard. Under a typical sparse experimental design, this mapping is ill-posed without relying on heavy regularization or prior distributions. Such assumptions may or may not be reliable, and can be hard to defend or test. In this paper, we take a close look at how to warrant a leap from past experiments to novel conditions based on minimal assumptions about the factorization of the distribution of the manipulated system, communicated in the well-understood language of factor graph models. A postulated $\textit{interventional factor model}$ (IFM) may not always be informative, but it conveniently abstracts away a need for explicit unmeasured confounding and feedback mechanisms, leading to directly testable claims. We derive necessary and sufficient conditions for causal effect identifiability with IFMs using data from a collection of experimental settings, and implement practical algorithms for generalizing expected outcomes to novel conditions never observed in the data.	['Ricardo Silva', 'Jakob Zeitler', 'Jialin Yu', 'David S. Watson', 'Gecia Bravo-Hermsdorff']	2023-06-06	null	null	null	null	['causal-inference', 'experimental-design', 'causal-inference']	['knowledge-base', 'methodology', 'miscellaneous']	[ 8.36162150e-01 1.11653440e-01 -4.75253880e-01 -1.76756874e-01 -2.63944864e-01 -6.17029369e-01 4.84966964e-01 3.62887353e-01 -2.44076550e-01 1.12082434e+00 7.71420971e-02 -9.91159797e-01 -9.36836362e-01 -5.23792624e-01 -1.17041218e+00 -6.82350576e-01 -4.79975492e-01 2.63809085e-01 -3.95930558e-01 1.61879152e-01 1.56030566e-01 6.46661997e-01 -1.38047338e+00 -3.39419752e-01 7.15613723e-01 4.53442931e-01 2.08788544e-01 6.89239025e-01 4.65926200e-01 4.99470145e-01 -3.56328517e-01 -7.39232674e-02 1.97379515e-01 -7.46858537e-01 -5.65920413e-01 1.05864041e-01 1.14101157e-01 -3.02298874e-01 -2.14202002e-01 9.58053350e-01 4.69591737e-01 2.00588688e-01 7.97034204e-01 -1.36306882e+00 -4.95466918e-01 6.98760986e-01 -4.56336290e-01 1.86429724e-01 2.61785567e-01 2.37141535e-01 9.74600971e-01 -4.16117072e-01 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a4099606-d1a6-428a-bd2e-ab99183a9440	cross-lingual-citations-in-english-papers-a	2111.05097	null	https://arxiv.org/abs/2111.05097v2	https://arxiv.org/pdf/2111.05097v2.pdf	Cross-Lingual Citations in English Papers: A Large-Scale Analysis of Prevalence, Usage, and Impact	Citation information in scholarly data is an important source of insight into the reception of publications and the scholarly discourse. Outcomes of citation analyses and the applicability of citation based machine learning approaches heavily depend on the completeness of such data. One particular shortcoming of scholarly data nowadays is that non-English publications are often not included in data sets, or that language metadata is not available. Because of this, citations between publications of differing languages (cross-lingual citations) have only been studied to a very limited degree. In this paper, we present an analysis of cross-lingual citations based on over one million English papers, spanning three scientific disciplines and a time span of three decades. Our investigation covers differences between cited languages and disciplines, trends over time, and the usage characteristics as well as impact of cross-lingual citations. Among our findings are an increasing rate of citations to publications written in Chinese, citations being primarily to local non-English languages, and consistency in citation intent between cross- and monolingual citations. To facilitate further research, we make our collected data and source code publicly available.	['Tornike Tsereteli', 'Michael Färber', 'Tarek Saier']	2021-11-07	null	null	null	null	['cross-lingual-entity-linking', 'citation-intent-classification']	['natural-language-processing', 'natural-language-processing']	[-6.86332107e-01 -5.09255111e-01 -1.04474556e+00 1.82961062e-01 -1.13885760e+00 -9.82172132e-01 1.30717862e+00 5.94959557e-01 -5.93685329e-01 9.48804855e-01 6.92065656e-01 -9.40717280e-01 -3.88283819e-01 -4.68631715e-01 -6.31244659e-01 -2.43543684e-01 5.30749202e-01 1.59274936e-01 -2.85192281e-01 2.00484186e-01 1.11361647e+00 4.08013612e-01 -1.22686207e+00 -3.48636717e-01 1.05620384e+00 2.96986818e-01 3.71193051e-01 9.41672921e-02 -9.55034077e-01 2.93171197e-01 -8.89113486e-01 -4.19866204e-01 -2.34729499e-01 -3.48198563e-01 -6.67675853e-01 -4.43127871e-01 5.87206542e-01 4.41206723e-01 -4.17829990e-01 8.70306015e-01 1.82930842e-01 -3.53509068e-01 6.96850300e-01 -8.28292966e-01 -1.08032835e+00 9.24487829e-01 -6.79618955e-01 6.24451816e-01 3.22184026e-01 -1.69091329e-01 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83cdfdd7-fd5f-4ec0-aa4c-9e8999d369e1	tfdet-target-aware-fusion-for-rgb-t	2305.16580	null	https://arxiv.org/abs/2305.16580v1	https://arxiv.org/pdf/2305.16580v1.pdf	TFDet: Target-aware Fusion for RGB-T Pedestrian Detection	Pedestrian detection is a critical task in computer vision because of its role in ensuring traffic safety. However, existing methods that rely solely on RGB images suffer from performance degradation under low-light conditions due to the lack of useful information. To address this issue, recent multispectral detection approaches combine thermal images to provide complementary information. Nevertheless, these approaches have limitations such as the noisy fused feature maps and the loss of informative features. In this paper, we propose a novel target-aware fusion strategy for multispectral pedestrian detection, named TFDet. Unlike existing methods, TFDet enhances features by supervising the fusion process with a correlation-maximum loss function. Our fusion strategy highlights the pedestrian-related features while suppressing the unrelated ones. TFDet achieves state-of-the-art performances on both KAIST and LLVIP benchmarks, with a speed comparable to the previous state-of-the-art counterpart. Importantly, TFDet performs remarkably well under low-light conditions, which is a significant advancement for road safety.	['Hui-Liang Shen', 'Zehua Sheng', 'Xiaohan Zhang', 'Xue Zhang']	2023-05-26	null	null	null	null	['pedestrian-detection']	['computer-vision']	[ 2.74875164e-01 -8.69732440e-01 2.61999965e-01 -2.08636492e-01 -8.22993279e-01 -2.64598846e-01 6.21212900e-01 6.27386793e-02 -7.58204281e-01 7.58718848e-01 -2.79170990e-01 -1.42524630e-01 2.39454865e-01 -8.55182827e-01 -4.09711927e-01 -1.26433170e+00 6.57751501e-01 -2.74893492e-01 8.25823247e-01 -3.36008310e-01 -8.32703188e-02 3.16598892e-01 -1.89026570e+00 9.01355445e-02 1.03207910e+00 1.04397309e+00 2.02613235e-01 3.93912643e-01 2.57693738e-01 3.19818467e-01 -3.61435831e-01 -6.62698984e-01 4.73315001e-01 -1.51473999e-01 -1.40242547e-01 1.19850270e-01 6.29816830e-01 -2.32437059e-01 -4.34279680e-01 1.26747894e+00 7.54087865e-01 1.65161327e-01 3.64430755e-01 -1.37406027e+00 -1.71157047e-01 -2.92406499e-01 -1.02521002e+00 3.27013642e-01 1.42808348e-01 5.74506640e-01 8.30489039e-01 -8.12515438e-01 9.29605681e-03 1.10699344e+00 5.54893374e-01 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37b69289-49fb-4ed9-8598-52d4cd85f980	improving-toponym-resolution-with-better	2305.11315	null	https://arxiv.org/abs/2305.11315v1	https://arxiv.org/pdf/2305.11315v1.pdf	Improving Toponym Resolution with Better Candidate Generation, Transformer-based Reranking, and Two-Stage Resolution	Geocoding is the task of converting location mentions in text into structured data that encodes the geospatial semantics. We propose a new architecture for geocoding, GeoNorm. GeoNorm first uses information retrieval techniques to generate a list of candidate entries from the geospatial ontology. Then it reranks the candidate entries using a transformer-based neural network that incorporates information from the ontology such as the entry's population. This generate-and-rerank process is applied twice: first to resolve the less ambiguous countries, states, and counties, and second to resolve the remaining location mentions, using the identified countries, states, and counties as context. Our proposed toponym resolution framework achieves state-of-the-art performance on multiple datasets. Code and models are available at \url{https://github.com/clulab/geonorm}.	['Steven Bethard', 'Zeyu Zhang']	2023-05-18	null	null	null	null	['toponym-resolution']	['natural-language-processing']	[-2.17765898e-01 1.65802911e-01 -3.03191334e-01 -3.99608910e-01 -1.01548696e+00 -5.05436182e-01 8.23486507e-01 8.25916231e-01 -4.50615525e-01 8.16811442e-01 1.04893184e+00 -3.70577067e-01 -1.49914518e-01 -1.25249600e+00 -5.68970203e-01 -1.55356333e-01 9.76954773e-02 5.53839087e-01 1.74872890e-01 -2.42452264e-01 4.61552501e-01 2.89674371e-01 -1.58309495e+00 3.31441909e-01 1.13853979e+00 1.00017822e+00 9.83269364e-02 1.90058321e-01 -6.52797222e-01 9.52168286e-01 -2.30072811e-01 -3.78106594e-01 1.80975229e-01 1.18992776e-01 -8.01277220e-01 -9.67600822e-01 4.66117769e-01 -1.62599176e-01 -3.81241560e-01 1.34658504e+00 2.62051642e-01 4.18830141e-02 6.70035243e-01 -1.06644654e+00 -1.04474211e+00 1.05000794e+00 -5.55961370e-01 2.27348626e-01 7.34170377e-01 -5.42214274e-01 1.11632991e+00 -1.01816893e+00 6.74856246e-01 9.87816513e-01 7.92498350e-01 -9.80322994e-03 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4ca5751e-806a-4b7a-9096-bfc3b1fcb15c	pose-disentangled-contrastive-learning-for	2211.13490	null	https://arxiv.org/abs/2211.13490v2	https://arxiv.org/pdf/2211.13490v2.pdf	Pose-disentangled Contrastive Learning for Self-supervised Facial Representation	Self-supervised facial representation has recently attracted increasing attention due to its ability to perform face understanding without relying on large-scale annotated datasets heavily. However, analytically, current contrastive-based self-supervised learning (SSL) still performs unsatisfactorily for learning facial representation. More specifically, existing contrastive learning (CL) tends to learn pose-invariant features that cannot depict the pose details of faces, compromising the learning performance. To conquer the above limitation of CL, we propose a novel Pose-disentangled Contrastive Learning (PCL) method for general self-supervised facial representation. Our PCL first devises a pose-disentangled decoder (PDD) with a delicately designed orthogonalizing regulation, which disentangles the pose-related features from the face-aware features; therefore, pose-related and other pose-unrelated facial information could be performed in individual subnetworks and do not affect each other's training. Furthermore, we introduce a pose-related contrastive learning scheme that learns pose-related information based on data augmentation of the same image, which would deliver more effective face-aware representation for various downstream tasks. We conducted linear evaluation on four challenging downstream facial understanding tasks, ie, facial expression recognition, face recognition, AU detection and head pose estimation. Experimental results demonstrate that our method significantly outperforms state-of-the-art SSL methods. Code is available at https://github.com/DreamMr/PCL}{https://github.com/DreamMr/PCL	['Shaoze Feng', 'Kejun Liu', 'Zhe Chen', 'Yibing Zhan', 'Wenbin Wang', 'Yuanyuan Liu']	2022-11-24	null	http://openaccess.thecvf.com//content/CVPR2023/html/Liu_Pose-Disentangled_Contrastive_Learning_for_Self-Supervised_Facial_Representation_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Liu_Pose-Disentangled_Contrastive_Learning_for_Self-Supervised_Facial_Representation_CVPR_2023_paper.pdf	cvpr-2023-1	['head-pose-estimation', 'facial-expression-recognition']	['computer-vision', 'computer-vision']	[ 2.42366735e-02 1.28780678e-01 -3.51287305e-01 -8.40252221e-01 -7.81485617e-01 -2.90426642e-01 5.22641599e-01 -4.79141563e-01 -5.35399653e-02 5.84358215e-01 3.82101953e-01 2.61283845e-01 -8.29438865e-02 -3.98224920e-01 -7.71319687e-01 -8.96171451e-01 -6.08716570e-02 2.32522249e-01 -5.66418052e-01 -3.06131274e-01 -2.26655364e-01 5.97959280e-01 -1.74290013e+00 2.64655858e-01 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