Multi-stream P&U adaptive graph convolutional networks for skeleton-based action recognition-Reference-Cited by-同舟云学术

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Multi-stream P&U adaptive graph convolutional networks for skeleton-based action recognition

Published:2024-01-29 Issue:8 Volume:80 Page:11614-11639
ISSN:0920-8542
Container-title:The Journal of Supercomputing
language:en
Short-container-title:J Supercomput

Author:

Chen Minglong,Liang Jiuzhen,Liu Hao

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11227-024-05900-9.pdf

Reference44 articles.

1. Yan S, Xiong Y, Lin D (2018) Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Thirty-second AAAI Conference on Artificial Intelligence. https://doi.org/10.48550/arXiv.1801.07455

2. Shi L, Zhang Y, Cheng J, Lu H (2019) Two-stream adaptive graph convolutional networks for skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 12026–12035. https://doi.org/10.48550/arXiv.1805.07694

3. Li M, Chen S, Chen X, Zhang Y, Wang Y, Tian Q (2019) Actional-structural graph convolutional networks for skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 3595–3603. https://doi.org/10.1109/CVPR.2019.01230

4. Gao X, Hu W, Tang J, Liu J, Guo Z (2019) Optimized skeleton-based action recognition via sparsified graph regression. In: Proceedings of the 27th ACM International Conference on Multimedia, pp 601–610. https://doi.org/10.48550/arXiv.1811.12013

5. Lee J, Lee M, Lee D, Lee S (2022) Hierarchically decomposed graph convolutional networks for skeleton-based action recognition. arXiv preprint arXiv:2208.10741

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