SelfGCN: Graph Convolution Network With Self-Attention for Skeleton-Based Action Recognition-Reference-Cited by-同舟云学术

SelfGCN: Graph Convolution Network With Self-Attention for Skeleton-Based Action Recognition

Published:2024 Issue: Volume:33 Page:4391-4403
ISSN:1057-7149
Container-title:IEEE Transactions on Image Processing
language:
Short-container-title:IEEE Trans. on Image Process.

Author:

Wu Zhize¹,Sun Pengpeng¹,Chen Xin¹^ORCID,Tang Keke²^ORCID,Xu Tong³^ORCID,Zou Le¹^ORCID,Wang Xiaofeng¹^ORCID,Tan Ming¹^ORCID,Cheng Fan⁴^ORCID,Weise Thomas¹^ORCID

Affiliation:

1. Institute of Applied Optimization, School of Artificial Intelligence and Big Data, Hefei University, Hefei, China

2. Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, China

3. School of Computer Science and Technology, University of Science and Technology of China, Hefei, China

4. Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei, China

Funder

Key Research Plan of Anhui Province

Project of Natural Science Foundation of Anhui Province

Open Fund of Information Materials and Intelligent Sensing Laboratory of Anhui Province

University Scientific Research Innovation Team Project of Anhui Province

Hefei Specially Recruited Foreign Expert Program and Hefei Foreign Expert Office Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/83/10346232/10618962.pdf?arnumber=10618962

Reference61 articles.

1. Together Recognizing, Localizing and Summarizing Actions in Egocentric Videos

2. Multimodal Gesture Recognition Based on the ResC3D Network

3. Human Action Recognition From Various Data Modalities: A Review

4. DG-STGCN: Dynamic spatial–temporal modeling for skeleton-based action recognition;Duan;arXiv:2210.05895,2022

5. Graph Convolutional Neural Network for Human Action Recognition: A Comprehensive Survey