DMMG: Dual Min-Max Games for Self-Supervised Skeleton-Based Action Recognition-Reference-Cited by-同舟云学术

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DMMG: Dual Min-Max Games for Self-Supervised Skeleton-Based Action Recognition

Published:2024 Issue: Volume:33 Page:395-407
ISSN:1057-7149
Container-title:IEEE Transactions on Image Processing
language:
Short-container-title:IEEE Trans. on Image Process.

Author:

Guan Shannan¹^ORCID,Yu Xin²^ORCID,Huang Wei³^ORCID,Fang Gengfa⁴,Lu Haiyan¹^ORCID

Affiliation:

1. Australia Artificial Intelligence Institute, University of Technology Sydney, Ultimo, NSW, Australia

2. School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD, Australia

3. RIKEN Center for Advanced Intelligence Project, Tokyo, Japan

4. School of Electrical and Data Engineering, University of Technology Sydney, Ultimo, NSW, Australia

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Graphics and Computer-Aided Design,Software

Link

http://xplorestaging.ieee.org/ielx7/83/10346232/10348505.pdf?arnumber=10348505

Reference59 articles.

1. A Comparative Review of Recent Kinect-Based Action Recognition Algorithms

2. Realtime Multi-person 2D Pose Estimation Using Part Affinity Fields

3. Boosting Monocular 3D Human Pose Estimation With Part Aware Attention

4. A Simple Yet Effective Baseline for 3d Human Pose Estimation

5. Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition

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