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cb29abcf-8c05-4830-8fef-5c786d1b1633 | attend-memorize-and-generate-towards-faithful-1 | 2203.00732 | null | https://arxiv.org/abs/2203.00732v1 | https://arxiv.org/pdf/2203.00732v1.pdf | Attend, Memorize and Generate: Towards Faithful Table-to-Text Generation in Few Shots | Few-shot table-to-text generation is a task of composing fluent and faithful sentences to convey table content using limited data. Despite many efforts having been made towards generating impressive fluent sentences by fine-tuning powerful pre-trained language models, the faithfulness of generated content still needs to be improved. To this end, this paper proposes a novel approach Attend, Memorize and Generate (called AMG), inspired by the text generation process of humans. In particular, AMG (1) attends over the multi-granularity of context using a novel strategy based on table slot level and traditional token-by-token level attention to exploit both the table structure and natural linguistic information; (2) dynamically memorizes the table slot allocation states; and (3) generates faithful sentences according to both the context and memory allocation states. Comprehensive experiments with human evaluation on three domains (i.e., humans, songs, and books) of the Wiki dataset show that our model can generate higher qualified texts when compared with several state-of-the-art baselines, in both fluency and faithfulness. | ['Philip S. Yu', 'Yao Wan', 'Ye Liu', 'Wenting Zhao'] | 2022-03-01 | attend-memorize-and-generate-towards-faithful | https://aclanthology.org/2021.findings-emnlp.347 | https://aclanthology.org/2021.findings-emnlp.347.pdf | findings-emnlp-2021-11 | ['table-to-text-generation'] | ['natural-language-processing'] | [ 2.48318046e-01 3.91110957e-01 -1.18735723e-01 -2.69467860e-01
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66d27375-2389-4888-9192-258c555567bb | weighted-anisotropic-isotropic-total | 2307.00439 | null | https://arxiv.org/abs/2307.00439v1 | https://arxiv.org/pdf/2307.00439v1.pdf | Weighted Anisotropic-Isotropic Total Variation for Poisson Denoising | Poisson noise commonly occurs in images captured by photon-limited imaging systems such as in astronomy and medicine. As the distribution of Poisson noise depends on the pixel intensity value, noise levels vary from pixels to pixels. Hence, denoising a Poisson-corrupted image while preserving important details can be challenging. In this paper, we propose a Poisson denoising model by incorporating the weighted anisotropic-isotropic total variation (AITV) as a regularization. We then develop an alternating direction method of multipliers with a combination of a proximal operator for an efficient implementation. Lastly, numerical experiments demonstrate that our algorithm outperforms other Poisson denoising methods in terms of image quality and computational efficiency. | ['Jack Xin', 'Fredrick Park', 'Yifei Lou', 'Kevin Bui'] | 2023-07-01 | null | null | null | null | ['astronomy'] | ['miscellaneous'] | [ 4.72609639e-01 -5.39900661e-01 3.25475872e-01 -5.54059558e-02
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bf18f094-75b8-49a3-a859-79e2b51ece54 | constructing-colloquial-dataset-for-persian | 2306.12679 | null | https://arxiv.org/abs/2306.12679v1 | https://arxiv.org/pdf/2306.12679v1.pdf | Constructing Colloquial Dataset for Persian Sentiment Analysis of Social Microblogs | Introduction: Microblogging websites have massed rich data sources for sentiment analysis and opinion mining. In this regard, sentiment classification has frequently proven inefficient because microblog posts typically lack syntactically consistent terms and representatives since users on these social networks do not like to write lengthy statements. Also, there are some limitations to low-resource languages. The Persian language has exceptional characteristics and demands unique annotated data and models for the sentiment analysis task, which are distinctive from text features within the English dialect. Method: This paper first constructs a user opinion dataset called ITRC-Opinion by collaborative environment and insource way. Our dataset contains 60,000 informal and colloquial Persian texts from social microblogs such as Twitter and Instagram. Second, this study proposes a new deep convolutional neural network (CNN) model for more effective sentiment analysis of colloquial text in social microblog posts. The constructed datasets are used to evaluate the presented model. Furthermore, some models, such as LSTM, CNN-RNN, BiLSTM, and BiGRU with different word embeddings, including Fasttext, Glove, and Word2vec, investigated our dataset and evaluated the results. Results: The results demonstrate the benefit of our dataset and the proposed model (72% accuracy), displaying meaningful improvement in sentiment classification performance. | ['Zeinab Rajabi', 'Farzaneh Rahmani', 'Leyla Rabiei', 'Mojtaba Mazoochi'] | 2023-06-22 | null | null | null | null | ['word-embeddings', 'sentiment-analysis', 'opinion-mining', 'persian-sentiment-anlysis'] | ['methodology', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-6.57622278e-01 -1.94170043e-01 -1.49049228e-02 -5.08473754e-01
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ed129eb1-3528-4e9e-a385-bd189043bbb0 | self-supervised-sparse-to-dense-motion | 2008.07872 | null | https://arxiv.org/abs/2008.07872v1 | https://arxiv.org/pdf/2008.07872v1.pdf | Self-supervised Sparse to Dense Motion Segmentation | Observable motion in videos can give rise to the definition of objects moving with respect to the scene. The task of segmenting such moving objects is referred to as motion segmentation and is usually tackled either by aggregating motion information in long, sparse point trajectories, or by directly producing per frame dense segmentations relying on large amounts of training data. In this paper, we propose a self supervised method to learn the densification of sparse motion segmentations from single video frames. While previous approaches towards motion segmentation build upon pre-training on large surrogate datasets and use dense motion information as an essential cue for the pixelwise segmentation, our model does not require pre-training and operates at test time on single frames. It can be trained in a sequence specific way to produce high quality dense segmentations from sparse and noisy input. We evaluate our method on the well-known motion segmentation datasets FBMS59 and DAVIS16. | ['Margret Keuper', 'Kalun Ho', 'Peter Ochs', 'Amirhossein Kardoost'] | 2020-08-18 | null | null | null | null | ['motion-segmentation'] | ['computer-vision'] | [ 4.56552863e-01 7.98271075e-02 -2.72133380e-01 -3.20848197e-01
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176dabcc-9aa8-44f7-b4b9-be82ed58c7c1 | visual-sentiment-prediction-with-deep | 1411.5731 | null | http://arxiv.org/abs/1411.5731v1 | http://arxiv.org/pdf/1411.5731v1.pdf | Visual Sentiment Prediction with Deep Convolutional Neural Networks | Images have become one of the most popular types of media through which users
convey their emotions within online social networks. Although vast amount of
research is devoted to sentiment analysis of textual data, there has been very
limited work that focuses on analyzing sentiment of image data. In this work,
we propose a novel visual sentiment prediction framework that performs image
understanding with Deep Convolutional Neural Networks (CNN). Specifically, the
proposed sentiment prediction framework performs transfer learning from a CNN
with millions of parameters, which is pre-trained on large-scale data for
object recognition. Experiments conducted on two real-world datasets from
Twitter and Tumblr demonstrate the effectiveness of the proposed visual
sentiment analysis framework. | ['Li-Jia Li', 'Kuang-Chih Lee', 'Suleyman Cetintas', 'Can Xu'] | 2014-11-21 | null | null | null | null | ['visual-sentiment-prediction'] | ['computer-vision'] | [ 1.15398735e-01 -1.94553539e-01 -2.85752833e-01 -6.62082732e-01
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19b1de35-291e-4378-95cc-9fb77a431a90 | parallelizing-legendre-memory-unit-training | 2102.11417 | null | https://arxiv.org/abs/2102.11417v2 | https://arxiv.org/pdf/2102.11417v2.pdf | Parallelizing Legendre Memory Unit Training | Recently, a new recurrent neural network (RNN) named the Legendre Memory Unit (LMU) was proposed and shown to achieve state-of-the-art performance on several benchmark datasets. Here we leverage the linear time-invariant (LTI) memory component of the LMU to construct a simplified variant that can be parallelized during training (and yet executed as an RNN during inference), thus overcoming a well known limitation of training RNNs on GPUs. We show that this reformulation that aids parallelizing, which can be applied generally to any deep network whose recurrent components are linear, makes training up to 200 times faster. Second, to validate its utility, we compare its performance against the original LMU and a variety of published LSTM and transformer networks on seven benchmarks, ranging from psMNIST to sentiment analysis to machine translation. We demonstrate that our models exhibit superior performance on all datasets, often using fewer parameters. For instance, our LMU sets a new state-of-the-art result on psMNIST, and uses half the parameters while outperforming DistilBERT and LSTM models on IMDB sentiment analysis. | ['Chris Eliasmith', 'Narsimha Chilkuri'] | 2021-02-22 | parallelizing-legendre-memory-unit-training-1 | https://arxiv.org/abs/2102.11417 | https://arxiv.org/pdf/2102.11417.pdf | null | ['sequential-image-classification'] | ['computer-vision'] | [ 5.45695312e-02 -7.23649487e-02 -2.27879882e-01 -1.71583742e-01
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adecbd42-75ff-4f6c-a9b1-7f6083914c26 | a-comprehensive-evaluation-on-multi-channel | 2202.10286 | null | https://arxiv.org/abs/2202.10286v1 | https://arxiv.org/pdf/2202.10286v1.pdf | A Comprehensive Evaluation on Multi-channel Biometric Face Presentation Attack Detection | The vulnerability against presentation attacks is a crucial problem undermining the wide-deployment of face recognition systems. Though presentation attack detection (PAD) systems try to address this problem, the lack of generalization and robustness continues to be a major concern. Several works have shown that using multi-channel PAD systems could alleviate this vulnerability and result in more robust systems. However, there is a wide selection of channels available for a PAD system such as RGB, Near Infrared, Shortwave Infrared, Depth, and Thermal sensors. Having a lot of sensors increases the cost of the system, and therefore an understanding of the performance of different sensors against a wide variety of attacks is necessary while selecting the modalities. In this work, we perform a comprehensive study to understand the effectiveness of various imaging modalities for PAD. The studies are performed on a multi-channel PAD dataset, collected with 14 different sensing modalities considering a wide range of 2D, 3D, and partial attacks. We used the multi-channel convolutional network-based architecture, which uses pixel-wise binary supervision. The model has been evaluated with different combinations of channels, and different image qualities on a variety of challenging known and unknown attack protocols. The results reveal interesting trends and can act as pointers for sensor selection for safety-critical presentation attack detection systems. The source codes and protocols to reproduce the results are made available publicly making it possible to extend this work to other architectures. | ['Sebastien Marcel', 'David Geissbuhler', 'Anjith George'] | 2022-02-21 | null | null | null | null | ['face-presentation-attack-detection'] | ['computer-vision'] | [ 3.95066291e-01 -3.70497495e-01 -7.29599819e-02 -1.79519325e-01
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af7a4476-939d-4120-af30-9c1f6cafaf3b | disclip-open-vocabulary-referring-expression | 2305.19108 | null | https://arxiv.org/abs/2305.19108v1 | https://arxiv.org/pdf/2305.19108v1.pdf | DisCLIP: Open-Vocabulary Referring Expression Generation | Referring Expressions Generation (REG) aims to produce textual descriptions that unambiguously identifies specific objects within a visual scene. Traditionally, this has been achieved through supervised learning methods, which perform well on specific data distributions but often struggle to generalize to new images and concepts. To address this issue, we present a novel approach for REG, named DisCLIP, short for discriminative CLIP. We build on CLIP, a large-scale visual-semantic model, to guide an LLM to generate a contextual description of a target concept in an image while avoiding other distracting concepts. Notably, this optimization happens at inference time and does not require additional training or tuning of learned parameters. We measure the quality of the generated text by evaluating the capability of a receiver model to accurately identify the described object within the scene. To achieve this, we use a frozen zero-shot comprehension module as a critique of our generated referring expressions. We evaluate DisCLIP on multiple referring expression benchmarks through human evaluation and show that it significantly outperforms previous methods on out-of-domain datasets. Our results highlight the potential of using pre-trained visual-semantic models for generating high-quality contextual descriptions. | ['Gal Chechik', 'Ethan Fetaya', 'Aviv Shamsian', 'Eitan Shaar', 'Lior Bracha'] | 2023-05-30 | null | null | null | null | ['referring-expression-generation', 'referring-expression'] | ['computer-vision', 'computer-vision'] | [ 4.60099041e-01 2.47159317e-01 2.82816049e-02 -5.95390439e-01
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