[1] NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis
- intro: CVPR 2016, NTU RGB-D 60 Dataset, Part-Aware LSTM, Benchmark Evaluation
- arxiv: https://arxiv.org/abs/1604.02808
- github: https://github.com/shahroudy/NTURGB-D
- github(TF): https://github.com/FesianXu/PLSTM
[2] Spatio-Temporal LSTM with Trust Gates for 3D Human Action Recognition
- intro: T-PAMI 2017, Spatio-Temporal LSTM
- arxiv: https://arxiv.org/abs/1607.07043
- github: https://github.com/kinect59/Spatio-Temporal-LSTM
[3] Skeleton Based Human Action Recognition with Global Context-Aware Attention LSTM Networks
- intro: CVPR 2017, Attention mechanism
- arxiv: https://arxiv.org/abs/1707.05740
[4] NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding
- intro: TPAMI 2019, NTU RGB-D 120 Dataset, One shot 3D Action Recognition
- dataset:[http://rose1.ntu.edu.sg/datasets/actionrecognition.asp](NTU RGB-D 120)
- arxiv: https://arxiv.org/pdf/1905.04757.pdf
- github: https://github.com/shahroudy/NTURGB-D
[5] Modeling Temporal Dynamics and Spatial Configurations of Actions Using Two-Stream Recurrent Neural Networks
- intro: CVPR 2017, Temporal RNN(Stacked RNN and Hierarchical RNN), Spatial RNN(Chain sequence and Traversal sequence)
- arxiv: https://arxiv.org/abs/1704.02581
- github: https://github.com/hongsong-wang/RNN-for-skeletons
[6] Learning content and style: Joint action recognition and person identification from human skeletons
- intro: PR 2018, Multi-task learning about action recognition and person identification
- github: https://github.com/hongsong-wang/Beyond-Joints
- intro: ICCV 2017, Refine NTU-D dataset
- github: https://github.com/InwoongLee/TS-LSTM
[8] View Adaptive Recurrent Neural Networks for High Performance Human Action Recognition from Skeleton Data
- intro: ICCV 2017, View Adaptation Subnetwork (RNN)
- arxiv: https://arxiv.org/abs/1703.08274
[9] View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition
- intro: View Adaptation Subnetwork (RNN and CNN)
- arxiv: http://cn.arxiv.org/abs/1804.07453
[10] Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN
- intro: CVPR 2018, Introduce the residual connectins to RNN
- arxiv: http://cn.arxiv.org/abs/1803.04831
- github(TF): https://github.com/batzner/indrnn
- github(Pytorch): https://github.com/StefOe/indrnn-pytorch
[11] Skeleton-Based Action Recognition with Spatial Reasoning and Temporal Stack Learning
- intro: ECCV 2018, Spatial reasoning network, Temporal stack learning network
- arxiv: https://arxiv.org/abs/1805.02335v1
[12] Adding Attentiveness to the Neurons in Recurrent Neural Networks
- intro: ECCV 2018, Element-wise-Attention Gate for an RNN Block
- arxiv: https://arxiv.org/abs/1807.04445v1
[1] Skeleton-based Action Recognition with Convolutional Neural Networks
- intro: ICMEW 2017, Two stream cnn, Transformer
- arxiv: https://arxiv.org/abs/1704.07595
[2] Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical Aggregation
- intro: IJCAI 2018, Hierarchical co-occurrence feature, Transposing
- arxiv: https://arxiv.org/abs/1804.06055
[3] Skeleton-Based Action Recognition with Synchronous Local and Non-local Spatio-temporal Learning and Frequency Attention
- intro: Frequency domain analysis, Non-local operation, Soft-margin focal loss, Transform Network
- arxiv: https://arxiv.org/abs/1811.04237
- intro: CVPRW 2019, Multi-feature CNN, Muti-task and Ensemble
[1] Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
- intro: AAAI 2018, Graph convolutional networks
- arxiv: https://arxiv.org/pdf/1801.07455.pdf
- github: https://github.com/yysijie/st-gcn
[2] Deep Progressive Reinforcement Learning for Skeleton-based Action Recognition
- intro: CVPR 2018, Using reinforcement learning to select frames
[3] Non-Local Graph Convolutional Networks for Skeleton-Based Action Recognition
- intro: Two stream gcn, Non-Local network
- arxiv: https://arxiv.org/abs/1805.07694v2
[4] Actional-Structural Graph Convolutional Networks for Skeleton-based Action Recognition
- intro: CVPR 2019, Actional-Structural GCN
- arxiv: https://arxiv.org/pdf/1904.12659.pdf
- github: https://github.com/limaosen0/AS-GCN
[5] An Attention Enhanced Graph Convolutional LSTM Network for Skeleton-Based Action Recognition
- intro: CVPR 2019, Graph Convolutional LSTM, Attention, Two part
- arxiv: https://arxiv.org/pdf/1902.09130.pdf
[6] Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition
[7] JOINTS RELATION INFERENCE NETWORK FOR SKELETON-BASED ACTION RECOGNITION
- intro: ICIP 2019, GCN+CNN, Optimal adjacency matrices
Other GITHUB Repos for Skeleton-based Action Recognition Papers and Small Notes
- https://github.com/cagbal/Skeleton-Based-Action-Recognition-Papers-and-Notes
- https://github.com/niais/Awesome-Skeleton-based-Action-Recognition
| Updated: 2019/10/11 |
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