Motion-Driven Spatial and Temporal Adaptive High-Resolution Graph Convolutional Networks for Skeleton-Based Action Recognition-Reference-Cited by-同舟云学术

Motion-Driven Spatial and Temporal Adaptive High-Resolution Graph Convolutional Networks for Skeleton-Based Action Recognition

Author:

Huang Zengxi¹,Qin Yusong¹,Lin Xiaobing²^ORCID,Liu Tianlin¹,Feng Zhenhua³^ORCID,Liu Yiguang⁴

Affiliation:

1. School of Computer Science and Software Engineering, and the Sichuan Xihua Jiaotong Forensics Center, Xihua University, Chengdu, China

2. School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, China

3. School of Computer Science and Electronic Engineering, University of Surrey, Guildford, U.K.

4. College of Computer Science, Sichuan University, Chengdu, China

Funder

National Natural Science Foundation of China

Sichuan Science and Technology Program

Xihua University Funds for Young Scholars

Sichuan University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Media Technology

Link

Reference83 articles.

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4. SequentialPointNet: A strong parallelized point cloud sequence network for 3D action recognition;li;arXiv 2111 08492,2021

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