Deformable graph convolutional transformer for skeleton-based action recognition
Author:
Funder
the Nature Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence
Link
https://link.springer.com/content/pdf/10.1007/s10489-022-04302-9.pdf
Reference52 articles.
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2. Vemulapalli R, Arrate F, Chellappa R (2014) Human action recognition by representing 3D skeletons as points in a lie group. In: 2014 IEEE Conference on computer vision and pattern recognition, CVPR 2014, Columbus, OH, USA, June 23-28, 2014, pp 588–595
3. Jiang X, Xu K, Sun T (2020) Action recognition scheme based on skeleton representation with DS-LSTM network. IEEE Trans Circuits Syst Video Technol 30(7):2129–2140
4. Song S, Lan C, Xing J, Zeng W, Liu J (2017) An End-to-End Spatio-Temporal Attention Model for Human Action Recognition from Skeleton Data. In: Singh, S.P., Markovitch, S. (eds.) Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, February 4-9, 2017, San Francisco, California, USA, pp 4263–4270
5. Zhang P, Lan C, Xing J, Zeng W, Xue J, Zheng N (2017) View adaptive recurrent neural networks for high performance human action recognition from skeleton data. In: IEEE International conference on computer vision, ICCV 2017, Venice, Italy, October 22-29, 2017, pp 2136–2145
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