Skeleton-based action recognition with multi-stream, multi-scale dilated spatial-temporal graph convolution network

Author:

Zhang Haiping,Liu XuORCID,Yu Dongjin,Guan Liming,Wang Dongjing,Ma Conghao,Hu Zepeng

Funder

Hangzhou Dianzi University

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence

Reference68 articles.

1. Abu-El-Haija S, Perozzi B, Kapoor A et al (2019) MixHop: higher-order graph convolutional architectures via sparsified neighborhood mixing. In: Chaudhuri K, Salakhutdinov R (eds) Proceedings of the 36th international conference on machine learning, proceedings of machine learning research. https://proceedings.mlr.press/v97/abu-el-haija19a.html, vol 97. PMLR, pp 21–29

2. Aggarwal JK, Ryoo MS (2011) Human activity analysis: a review. Acm Computing Surveys (Csur) 43(3):1–43

3. Alsarhan T, Ali U, Lu H (2022) Enhanced discriminative graph convolutional network with adaptive temporal modelling for skeleton-based action recognition. Comput Vis Image Underst 216:103,348. https://doi.org/10.1016/j.cviu.2021.103348. https://www.sciencedirect.com/science/article/pii/S107731422100179X

4. Atwood J, Towsley D (2016) Diffusion-convolutional neural networks. Advances in Neural Information Processing Systems 29

5. Bai S, Kolter JZ, Koltun V (2018) An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv:1803.01271

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