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Spatial Graph Convolutional and Temporal Involution Network for Skeleton-based Action Recognition

Published: 02 October 2021 Publication History
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References

[1]
Carlos Caetano, Francois Bremond, and William Robson Schwartz. 2019. Skeleton image representation for 3d action recognition based on tree structure and reference joints. Proc. - 32nd Conf. Graph. Patterns Images, SIBGRAPI 2019 (2019), 16–23.
[2]
Carlos Caetano, Jessica Sena, Franois Brmond, Jefersson A. dos Santos, and William Robson Schwartz. 2019. SkeleMotion: A new representation of skeleton joint sequences based on motion information for 3D action recognition. arXiv (2019).
[3]
Navneet Dalal, Bill Triggs, Navneet Dalal, and Bill Triggs. 2005. Histograms of Oriented Gradients for Human Detection To cite this version: Histograms of Oriented Gradients for Human Detection. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. (2005), 886–893. Retrieved from http://lear.inrialpes.fr
[4]
Max Jaderberg, Andrea Vedaldi, and Andrew Zisserman. 2014. Speeding up convolutional neural networks with low rank expansions. BMVC 2014 - Proc. Br. Mach. Vis. Conf. 2014 (2014).
[5]
Will Kay, João Carreira, Karen Simonyan, Brian Zhang, Chloe Hillier, Sudheendra Vijayanarasimhan, Fabio Viola, Tim Green, Trevor Back, Paul Natsev, Mustafa Suleyman, and Andrew Zisserman. 2017. The Kinetics human action video dataset. arXiv (2017).
[6]
Tae Soo Kim and Austin Reiter. 2017. Interpretable 3D Human Action Analysis with Temporal Convolutional Networks. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. Work. 2017-July, (2017), 1623–1631.
[7]
Duo Li, Jie Hu, Changhu Wang, Xiangtai Li, Qi She, Lei Zhu, Tong Zhang, and Qifeng Chen. 2021. Involution: Inverting the Inherence of Convolution for Visual Recognition. (2021). Retrieved from http://arxiv.org/abs/2103.06255
[8]
Shuai Li, Wanqing Li, Chris Cook, Ce Zhu, and Yanbo Gao. 2018. Independently recurrent neural network (IndRNN): Building a longer and deeper RNN. arXiv (2018), 5457–5466.
[9]
Jun Liu, Amir Shahroudy, Dong Xu, and Gang Wang. 2016. Spatio-temporal LSTM with trust gates for 3D human action recognition. Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics) 9907 LNCS, (2016), 816–833.
[10]
Jun Liu, Gang Wang, Ling Yu Duan, Kamila Abdiyeva, and Alex C. Kot. 2018. Skeleton-Based Human Action Recognition with Global Context-Aware Attention LSTM Networks. IEEE Trans. Image Process. 27, 4 (2018), 1586–1599.
[11]
Mengyuan Liu, Hong Liu, and Chen Chen. 2017. Enhanced skeleton visualization for view invariant human action recognition. Pattern Recognit. 68, (2017), 346–362.
[12]
Wei Peng, Xiaopeng Hong, Haoyu Chen, and Guoying Zhao. 2019. Learning graph convolutional network for skeleton-based human action recognition by neural searching. arXiv (2019).
[13]
Amir Shahroudy, Jun Liu, Tian Tsong Ng, and Gang Wang. 2016. NTU RGB+D: A large scale dataset for 3D human activity analysis. Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. 2016-Decem, (2016), 1010–1019.
[14]
Lei Shi, Yifan Zhang, Jian Cheng, and Hanqing Lu. 2019. Two-stream adaptive graph convolutional networks for skeleton-based action recognition. Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. 2019-June, (2019), 12018–12027.
[15]
Yi-Fan Song, Zhang Zhang, Caifeng Shan, and Liang Wang. 2020. Stronger, Faster and More Explainable: A Graph Convolutional Baseline for Skeleton-based Action Recognition. (2020), 1625–1633.
[16]
Yi Fan Song, Zhang Zhang, and Liang Wang. 2019. Richly Activated Graph Convolutional Network for Action Recognition with Incomplete Skeletons. Proc. - Int. Conf. Image Process. ICIP 2019-Septe, (2019), 1–5.
[17]
Yansong Tang, Yi Tian, Jiwen Lu, Peiyang Li, and Jie Zhou. 2018. Deep Progressive Reinforcement Learning for Skeleton-Based Action Recognition. Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. (2018), 5323–5332.
[18]
Heng Wang, Alexander Kläser, Cordelia Schmid, and Cheng Lin Liu. 2013. Dense trajectories and motion boundary descriptors for action recognition. Int. J. Comput. Vis. 103, 1 (2013), 60–79.
[19]
Heng Wang and Cordelia Schmid. 2013. Action recognition with improved trajectories. Proc. IEEE Int. Conf. Comput. Vis. (2013), 3551–3558.
[20]
Yu Hui Wen, Lin Gao, Hongbo Fu, Fang Lue Zhang, and Shihong Xia. 2019. Graph CNNs with motif and variable temporal block for skeleton-based action recognition. 33rd AAAI Conf. Artif. Intell. AAAI 2019, 31st Innov. Appl. Artif. Intell. Conf. IAAI 2019 9th AAAI Symp. Educ. Adv. Artif. Intell. EAAI 2019 (2019), 8989–8996.
[21]
Sijie Yan, Yuanjun Xiong, and Dahua Lin. 2018. Spatial temporal graph convolutional networks for skeleton-based action recognition. 32nd AAAI Conf. Artif. Intell. AAAI 2018 (2018), 7444–7452.
[22]
Wu Zheng, Lin Li, Zhaoxiang Zhang, Yan Huang, and Liang Wang. 2019. Relational network for skeleton-based action recognition. Proc. - IEEE Int. Conf. Multimed. Expo 2019-July, (2019), 826–831.

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    ACM TURC '21: Proceedings of the ACM Turing Award Celebration Conference - China
    July 2021
    284 pages
    ISBN:9781450385671
    DOI:10.1145/3472634
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    Published: 02 October 2021

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    Author Tags

    1. Action Recognition
    2. GCNs
    3. Involution
    4. Skeleton

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