https://github.com/XiaoCode-er/Skeleton-Based-Action-Recognition-Papers

The paper list about skeleton-based action recognition.
https://github.com/XiaoCode-er/Skeleton-Based-Action-Recognition-Papers

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The paper list about skeleton-based action recognition.

• Host: GitHub
• URL: https://github.com/XiaoCode-er/Skeleton-Based-Action-Recognition-Papers
• Owner: XiaoCode-er
• Created: 2018-09-10T05:18:24.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2019-10-11T10:03:48.000Z (over 5 years ago)
• Last Synced: 2024-11-13T05:36:14.946Z (7 months ago)
• Homepage:
• Size: 47.9 KB
• Stars: 282
• Watchers: 16
• Forks: 51
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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• Awesome-Skeleton-based-Action-Recognition - <https://github.com/XiaoCode-er/Skeleton-Based-Action-Recognition-Papers>

README

        
# Papers

## RNN

***

### NTU

**[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](https://arxiv.org/abs/1604.02808)

- github: [https://github.com/shahroudy/NTURGB-D](https://github.com/shahroudy/NTURGB-D)

- github(TF): [https://github.com/FesianXu/PLSTM](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](https://arxiv.org/abs/1607.07043)

- github: [https://github.com/kinect59/Spatio-Temporal-LSTM](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](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](https://arxiv.org/pdf/1905.04757.pdf)

- github: [https://github.com/shahroudy/NTURGB-D](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](https://arxiv.org/abs/1704.02581)

- github: [https://github.com/hongsong-wang/RNN-for-skeletons](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](https://github.com/hongsong-wang/Beyond-Joints)

[**[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)

- intro: ICCV 2017, Refine NTU-D dataset

- github: [https://github.com/InwoongLee/TS-LSTM](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](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](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](http://cn.arxiv.org/abs/1803.04831)

- github(TF): [https://github.com/batzner/indrnn](https://github.com/batzner/indrnn)

- github(Pytorch): [https://github.com/StefOe/indrnn-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](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](https://arxiv.org/abs/1807.04445v1)

## CNN

***

### Hikvision

**[1] Skeleton-based Action Recognition with Convolutional Neural Networks**

- intro: ICMEW 2017, Two stream cnn, Transformer

- arxiv: [https://arxiv.org/abs/1704.07595](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](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](https://arxiv.org/abs/1811.04237)

[**[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)

- intro: CVPRW 2019, Multi-feature CNN, Muti-task and Ensemble 

## GCN

**[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](https://arxiv.org/pdf/1801.07455.pdf)

- github: [https://github.com/yysijie/st-gcn](https://github.com/yysijie/st-gcn)

[**[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)

- 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](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](https://arxiv.org/pdf/1904.12659.pdf)

- github: [https://github.com/limaosen0/AS-GCN](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](https://arxiv.org/pdf/1902.09130.pdf)

 **[6] Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition**

- github: [https://github.com/lshiwjx/2s-AGCN](https://github.com/lshiwjx/2s-AGCN)

[**[7] JOINTS RELATION INFERENCE NETWORK FOR SKELETON-BASED ACTION RECOGNITION**](https://ieeexplore.ieee.org/document/8802912)

- 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/cagbal/Skeleton-Based-Action-Recognition-Papers-and-Notes)

- [https://github.com/niais/Awesome-Skeleton-based-Action-Recognition](https://github.com/niais/Awesome-Skeleton-based-Action-Recognition)

***

| Updated: 2019/10/11|

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