AeS‐GCN: Attention‐enhanced semantic‐guided graph convolutional networks for skeleton‐based action recognition-Reference-Cited by-同舟云学术

AeS‐GCN: Attention‐enhanced semantic‐guided graph convolutional networks for skeleton‐based action recognition

Published:2022-06 Issue:3-4 Volume:33 Page:
ISSN:1546-4261
Container-title:Computer Animation and Virtual Worlds
language:en
Short-container-title:Computer Animation & Virtual

Author:

Xu Qing¹^ORCID,Liu Feng²³⁴⁵^ORCID,Fu Ziwang¹,Zhou Aimin²³⁴,Qi Jiayin⁵

Affiliation:

1. School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing China

2. Institute of AI for Education East China Normal University Shanghai China

3. School of Computer Science and Technology East China Normal University Shanghai China

4. Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, School of Psychology and Cognitive Science East China Normal University Shanghai China

5. Institute of Artificial Intelligence and Change Management Shanghai University of International Business and Economics Shanghai China

Publisher

Wiley

Subject

Computer Graphics and Computer-Aided Design,Software

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/cav.2070

Reference23 articles.

1. ZhangY ZhaoZ LiW DuanL.Multi‐scale enhanced active learning for skeleton‐based action recognition. Proceedings of the 2021 IEEE International Conference on Multimedia and Expo (ICME) Shenzhen China. IEEE;2021. p.1–6.

2. YouD WangL HanD LiangS LiuH YuanF.Spatial reasoning and context‐aware attention network for skeleton‐based action recognition. Proceedings of the 2021 IEEE International Conference on Multimedia and Expo (ICME) Shenzhen China. IEEE; 2021. p. 1–6.

3. YanS XiongY LinD.Spatial temporal graph convolutional networks for skeleton‐based action recognition. Proceedings of the 32nd AAAI Conference on Artificial Intelligence New Orleans Louisiana USA; 2018.

4. ShahroudyA LiuJ NgTT WangG.Ntu rgb+ d: a large scale dataset for 3D human activity analysis. Proceedings of the IEEE Conference on CVPR Las Vegas NV USA;2016. p.1010–9.

5. NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding

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