{"id":13699829,"url":"https://github.com/XiaoCode-er/Skeleton-Based-Action-Recognition-Papers","last_synced_at":"2025-05-04T17:30:55.975Z","repository":{"id":216810254,"uuid":"148102723","full_name":"XiaoCode-er/Skeleton-Based-Action-Recognition-Papers","owner":"XiaoCode-er","description":"The paper list about skeleton-based action 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Resources"],"sub_categories":["2014"],"readme":"# Papers\n## RNN\n***\n### NTU\n**[1] NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis**\n- intro: CVPR 2016, NTU RGB-D 60 Dataset, Part-Aware LSTM, Benchmark Evaluation\n- arxiv: [https://arxiv.org/abs/1604.02808](https://arxiv.org/abs/1604.02808)\n- github: [https://github.com/shahroudy/NTURGB-D](https://github.com/shahroudy/NTURGB-D)\n- github(TF): [https://github.com/FesianXu/PLSTM](https://github.com/FesianXu/PLSTM)\n\n**[2] Spatio-Temporal LSTM with Trust Gates for 3D Human Action Recognition**\n- intro: T-PAMI 2017, Spatio-Temporal LSTM\n- arxiv: [https://arxiv.org/abs/1607.07043](https://arxiv.org/abs/1607.07043)\n- github: [https://github.com/kinect59/Spatio-Temporal-LSTM](https://github.com/kinect59/Spatio-Temporal-LSTM)\n\n**[3] Skeleton Based Human Action Recognition with Global Context-Aware Attention LSTM Networks**\n- intro: CVPR 2017, Attention mechanism\n- arxiv: [https://arxiv.org/abs/1707.05740](https://arxiv.org/abs/1707.05740)\n\n**[4] NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding**\n- intro: TPAMI 2019, NTU RGB-D 120 Dataset, One shot 3D Action Recognition\n- dataset:[http://rose1.ntu.edu.sg/datasets/actionrecognition.asp](NTU RGB-D 120)\n- arxiv: [https://arxiv.org/pdf/1905.04757.pdf](https://arxiv.org/pdf/1905.04757.pdf)\n- github: [https://github.com/shahroudy/NTURGB-D](https://github.com/shahroudy/NTURGB-D)\n\n***\n**[5] Modeling Temporal Dynamics and Spatial Configurations of Actions Using Two-Stream Recurrent Neural Networks**\n- intro: CVPR 2017, Temporal RNN(Stacked RNN and Hierarchical RNN), Spatial RNN(Chain sequence and Traversal sequence)\n- arxiv: [https://arxiv.org/abs/1704.02581](https://arxiv.org/abs/1704.02581)\n- github: [https://github.com/hongsong-wang/RNN-for-skeletons](https://github.com/hongsong-wang/RNN-for-skeletons)\n\n**[6] Learning content and style: Joint action recognition and person identification from human skeletons**\n- intro: PR 2018, Multi-task learning about action recognition and person identification\n- github: [https://github.com/hongsong-wang/Beyond-Joints](https://github.com/hongsong-wang/Beyond-Joints)\n\n[**[7] Ensemble Deep Learning for Skeleton-based Action Recognition using Temporal Sliding LSTM networks**](http://openaccess.thecvf.com/content_ICCV_2017/papers/Lee_Ensemble_Deep_Learning_ICCV_2017_paper.pdf)\n- intro: ICCV 2017, Refine NTU-D dataset\n- github: [https://github.com/InwoongLee/TS-LSTM](https://github.com/InwoongLee/TS-LSTM)\n\n**[8] View Adaptive Recurrent Neural Networks for High Performance Human Action Recognition from Skeleton Data**\n- intro: ICCV 2017, View Adaptation Subnetwork (RNN)\n- arxiv: [https://arxiv.org/abs/1703.08274](https://arxiv.org/abs/1703.08274)\n\n\n**[9] View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition**\n- intro: View Adaptation Subnetwork (RNN and CNN)\n- arxiv: [http://cn.arxiv.org/abs/1804.07453](http://cn.arxiv.org/abs/1804.07453)\n\n**[10] Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN**\n- intro: CVPR 2018, Introduce the residual connectins to RNN\n- arxiv: [http://cn.arxiv.org/abs/1803.04831](http://cn.arxiv.org/abs/1803.04831)\n- github(TF): [https://github.com/batzner/indrnn](https://github.com/batzner/indrnn)\n- github(Pytorch): [https://github.com/StefOe/indrnn-pytorch](https://github.com/StefOe/indrnn-pytorch)\n\n**[11] Skeleton-Based Action Recognition with Spatial Reasoning and Temporal Stack Learning**\n- intro: ECCV 2018, Spatial reasoning network, Temporal stack learning network\n- arxiv: [https://arxiv.org/abs/1805.02335v1](https://arxiv.org/abs/1805.02335v1)\n\n**[12] Adding Attentiveness to the Neurons in Recurrent Neural Networks**\n- intro: ECCV 2018, Element-wise-Attention Gate for an RNN Block\n- arxiv: [https://arxiv.org/abs/1807.04445v1](https://arxiv.org/abs/1807.04445v1)\n\n## CNN\n***\n### Hikvision\n**[1] Skeleton-based Action Recognition with Convolutional Neural Networks**\n- intro: ICMEW 2017, Two stream cnn, Transformer\n- arxiv: [https://arxiv.org/abs/1704.07595](https://arxiv.org/abs/1704.07595)\n\n**[2] Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical Aggregation**\n- intro: IJCAI 2018, Hierarchical co-occurrence feature, Transposing\n- arxiv: [https://arxiv.org/abs/1804.06055](https://arxiv.org/abs/1804.06055)\n***\n\n**[3] Skeleton-Based Action Recognition with Synchronous Local and Non-local Spatio-temporal Learning and Frequency Attention**\n- intro: Frequency domain analysis, Non-local operation, Soft-margin focal loss, Transform Network\n- arxiv: [https://arxiv.org/abs/1811.04237](https://arxiv.org/abs/1811.04237)\n\n[**[4] Three-Stream Convolutional Neural Network with Multi-task and Ensemble Learning for 3D Action Recognition**](http://202.200.119.253/cache/5/03/openaccess.thecvf.com/6c9ec35804b22272907c9b9298eaa0a1/Liang_Three-Stream_Convolutional_Neural_Network_With_Multi-Task_and_Ensemble_Learning_for_CVPRW_2019_paper.pdf)\n- intro: CVPRW 2019, Multi-feature CNN, Muti-task and Ensemble \n\n## GCN\n**[1] Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition**\n- intro: AAAI 2018, Graph convolutional networks\n- arxiv: [https://arxiv.org/pdf/1801.07455.pdf](https://arxiv.org/pdf/1801.07455.pdf)\n- github: [https://github.com/yysijie/st-gcn](https://github.com/yysijie/st-gcn)\n\n[**[2] Deep Progressive Reinforcement Learning for Skeleton-based Action Recognition**](http://openaccess.thecvf.com/content_cvpr_2018/html/Tang_Deep_Progressive_Reinforcement_CVPR_2018_paper.html)\n- intro: CVPR 2018, Using reinforcement learning to select frames \n \n **[3] Non-Local Graph Convolutional Networks for Skeleton-Based Action Recognition**\n- intro: Two stream gcn, Non-Local network\n- arxiv: [https://arxiv.org/abs/1805.07694v2](https://arxiv.org/abs/1805.07694v2)\n\n **[4] Actional-Structural Graph Convolutional Networks for Skeleton-based Action Recognition**\n- intro: CVPR 2019, Actional-Structural GCN\n- arxiv: [https://arxiv.org/pdf/1904.12659.pdf](https://arxiv.org/pdf/1904.12659.pdf)\n- github: [https://github.com/limaosen0/AS-GCN](https://github.com/limaosen0/AS-GCN)\n\n **[5] An Attention Enhanced Graph Convolutional LSTM Network for Skeleton-Based Action Recognition**\n- intro: CVPR 2019, Graph Convolutional LSTM, Attention, Two part\n- arxiv: [https://arxiv.org/pdf/1902.09130.pdf](https://arxiv.org/pdf/1902.09130.pdf)\n\n **[6] Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition**\n- github: [https://github.com/lshiwjx/2s-AGCN](https://github.com/lshiwjx/2s-AGCN)\n\n[**[7] JOINTS RELATION INFERENCE NETWORK FOR SKELETON-BASED ACTION RECOGNITION**](https://ieeexplore.ieee.org/document/8802912)\n- intro: ICIP 2019, GCN+CNN, Optimal adjacency matrices  \n\n***\n**Other GITHUB Repos for Skeleton-based Action Recognition Papers and Small Notes**\n- [https://github.com/cagbal/Skeleton-Based-Action-Recognition-Papers-and-Notes](https://github.com/cagbal/Skeleton-Based-Action-Recognition-Papers-and-Notes)\n- [https://github.com/niais/Awesome-Skeleton-based-Action-Recognition](https://github.com/niais/Awesome-Skeleton-based-Action-Recognition)\n***\n| Updated: 2019/10/11|\n| :---------: |\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FXiaoCode-er%2FSkeleton-Based-Action-Recognition-Papers","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FXiaoCode-er%2FSkeleton-Based-Action-Recognition-Papers","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FXiaoCode-er%2FSkeleton-Based-Action-Recognition-Papers/lists"}