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# Projects based on MMDetection |
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There are many projects built upon MMDetection. |
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We list some of them as examples of how to extend MMDetection for your own projects. |
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Pull requests are also welcomed. |
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## Projects as an extension |
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Some projects extend the boundary of MMDetection for deployment or other research fields. |
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They reveal the potential of what MMDetection can do. We list several of them as below. |
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- [OTEDetection](https://github.com/opencv/mmdetection): OpenVINO training extensions for object detection. |
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- [MMDetection3d](https://github.com/open-mmlab/mmdetection3d): OpenMMLab's next-generation platform for general 3D object detection. |
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## Projects of papers |
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There are also projects released with papers. |
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Some of the papers are published in top-tier conferences (CVPR, ICCV, and ECCV), the others are also highly influential. |
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To make this list also a reference for the community to develop and compare new object detection algorithms, we list them following the time order of top-tier conferences. |
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Methods already supported and maintained by MMDetection are not listed. |
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- Overcoming Classifier Imbalance for Long-tail Object Detection with Balanced Group Softmax, CVPR2020. [[paper]](http://openaccess.thecvf.com/content_CVPR_2020/papers/Li_Overcoming_Classifier_Imbalance_for_Long-Tail_Object_Detection_With_Balanced_Group_CVPR_2020_paper.pdf)[[github]](https://github.com/FishYuLi/BalancedGroupSoftmax) |
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- Coherent Reconstruction of Multiple Humans from a Single Image, CVPR2020. [[paper]](https://jiangwenpl.github.io/multiperson/)[[github]](https://github.com/JiangWenPL/multiperson) |
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- Look-into-Object: Self-supervised Structure Modeling for Object Recognition, CVPR 2020. [[paper]](http://openaccess.thecvf.com/content_CVPR_2020/papers/Zhou_Look-Into-Object_Self-Supervised_Structure_Modeling_for_Object_Recognition_CVPR_2020_paper.pdf)[[github]](https://github.com/JDAI-CV/LIO) |
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- Video Panoptic Segmentation, CVPR2020. [[paper]](https://arxiv.org/abs/2006.11339)[[github]](https://github.com/mcahny/vps) |
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- D2Det: Towards High Quality Object Detection and Instance Segmentation, CVPR2020. [[paper]](http://openaccess.thecvf.com/content_CVPR_2020/html/Cao_D2Det_Towards_High_Quality_Object_Detection_and_Instance_Segmentation_CVPR_2020_paper.html)[[github]](https://github.com/JialeCao001/D2Det) |
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- CentripetalNet: Pursuing High-quality Keypoint Pairs for Object Detection, CVPR2020. [[paper]](https://arxiv.org/abs/2003.09119)[[github]](https://github.com/KiveeDong/CentripetalNet) |
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- Learning a Unified Sample Weighting Network for Object Detection, CVPR 2020. [[paper]](http://openaccess.thecvf.com/content_CVPR_2020/html/Cai_Learning_a_Unified_Sample_Weighting_Network_for_Object_Detection_CVPR_2020_paper.html)[[github]](https://github.com/caiqi/sample-weighting-network) |
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- Scale-equalizing Pyramid Convolution for Object Detection, CVPR2020. [[paper]](https://arxiv.org/abs/2005.03101) [[github]](https://github.com/jshilong/SEPC) |
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- Revisiting the Sibling Head in Object Detector, CVPR2020. [[paper]](https://arxiv.org/abs/2003.07540)[[github]](https://github.com/Sense-X/TSD) |
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- PolarMask: Single Shot Instance Segmentation with Polar Representation, CVPR2020. [[paper]](https://arxiv.org/abs/1909.13226)[[github]](https://github.com/xieenze/PolarMask) |
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- Hit-Detector: Hierarchical Trinity Architecture Search for Object Detection, CVPR2020. [[paper]](https://arxiv.org/abs/2003.11818)[[github]](https://github.com/ggjy/HitDet.pytorch) |
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- ZeroQ: A Novel Zero Shot Quantization Framework, CVPR2020. [[paper]](https://arxiv.org/abs/2001.00281)[[github]](https://github.com/amirgholami/ZeroQ) |
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- CBNet: A Novel Composite Backbone Network Architecture for Object Detection, AAAI2020. [[paper]](https://aaai.org/Papers/AAAI/2020GB/AAAI-LiuY.1833.pdf)[[github]](https://github.com/VDIGPKU/CBNet) |
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- RDSNet: A New Deep Architecture for Reciprocal Object Detection and Instance Segmentation, AAAI2020. [[paper]](https://arxiv.org/abs/1912.05070)[[github]](https://github.com/wangsr126/RDSNet) |
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- Training-Time-Friendly Network for Real-Time Object Detection, AAAI2020. [[paper]](https://arxiv.org/abs/1909.00700)[[github]](https://github.com/ZJULearning/ttfnet) |
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- Cascade RPN: Delving into High-Quality Region Proposal Network with Adaptive Convolution, NeurIPS 2019. [[paper]](https://arxiv.org/abs/1909.06720)[[github]](https://github.com/thangvubk/Cascade-RPN) |
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- Reasoning R-CNN: Unifying Adaptive Global Reasoning into Large-scale Object Detection, CVPR2019. [[paper]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Xu_Reasoning-RCNN_Unifying_Adaptive_Global_Reasoning_Into_Large-Scale_Object_Detection_CVPR_2019_paper.pdf)[[github]](https://github.com/chanyn/Reasoning-RCNN) |
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- Learning RoI Transformer for Oriented Object Detection in Aerial Images, CVPR2019. [[paper]](https://arxiv.org/abs/1812.00155)[[github]](https://github.com/dingjiansw101/AerialDetection) |
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- SOLO: Segmenting Objects by Locations. [[paper]](https://arxiv.org/abs/1912.04488)[[github]](https://github.com/WXinlong/SOLO) |
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- SOLOv2: Dynamic, Faster and Stronger. [[paper]](https://arxiv.org/abs/2003.10152)[[github]](https://github.com/WXinlong/SOLO) |
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- Dense Peppoints: Representing Visual Objects with Dense Point Sets. [[paper]](https://arxiv.org/abs/1912.11473)[[github]](https://github.com/justimyhxu/Dense-RepPoints) |
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- IterDet: Iterative Scheme for Object Detection in Crowded Environments. [[paper]](https://arxiv.org/abs/2005.05708)[[github]](https://github.com/saic-vul/iterdet) |
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- Cross-Iteration Batch Normalization. [[paper]](https://arxiv.org/abs/2002.05712)[[github]](https://github.com/Howal/Cross-iterationBatchNorm) |
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- Pedestrian Detection: The Elephant In The Room. [[paper]](https://arxiv.org/abs/2003.08799)[[github]](https://github.com/hasanirtiza/Pedestron) |
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- A Ranking-based, Balanced Loss Function Unifying Classification and Localisation in Object Detection, NeurIPS2020 [[paper]](https://arxiv.org/abs/2009.13592)[[github]](https://github.com/kemaloksuz/aLRPLoss) |
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