Multi-granular spatial-temporal synchronous graph convolutional network for robust action recognition

Author:

Li ChangORCID,Huang QianORCID,Mao YingchiORCID,Li XingORCID,Wu JieORCID

Funder

Natural Science Research of Jiangsu Higher Education Institutions of China

Key Research and Development Program of Zhejiang Province

Bairen Program of Yunnan Province

High Level Innovation and Entrepreneurial Research Team Program in Jiangsu

Social Science Foundation of Jiangsu Province

Support Program for Longyuan Youth and Fundamental Research Funds for the Universities of Gansu Province

Hohai University

Publisher

Elsevier BV

Reference65 articles.

1. 2-d skeleton-based action recognition via two-branch stacked LSTM-RNNs;Avola;IEEE Transactions on Multimedia,2020

2. Channel-wise topology refinement graph convolution for skeleton-based action recognition;Chen,2021

3. Learning multi-granular spatio-temporal graph network for skeleton-based action recognition;Chen,2021

4. Decoupling GCN with DropGraph module for skeleton-based action recognition;Cheng,2020

5. Skeleton-based action recognition with shift graph convolutional network;Cheng,2020

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