Skeleton-based Action Recognition Using Two-stream Graph Convolutional Network with Pose Refinement
Author:
Affiliation:
1. University of Geosciences,School of Automation China,Wuhan,China,430074
2. University of Alberta,Department of Electrical and Computer Engineering,Edmonton,Canada
3. Tokyo Institute of Technology,Yokohama,Japan,226–8502
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9901509/9901494/09901587.pdf?arnumber=9901587
Reference16 articles.
1. Learning Spatiotemporal Features with 3D Convolutional Networks
2. Spatial Temporal Graph Con-volutional Networks for Skeleton-Based Action Recognition;yan;Innovative Applications of Artificial Intelligence Conference,2018
3. Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition;shi;Computer Vision and Pattern Recognition,2018
4. Semantics-Guided Neural Net-works for Efficient Skeleton-Based Human Action Recognition;zhang;Computer Vision and Pattern Recognition,2020
5. Realtime Multi-Person 2D Pose Estimation Using Part Afinity Fields;cao;Computer Vision and Pattern Recognition,2017
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1. Action recognition method based on multi-stream attention-enhanced recursive graph convolution;Applied Intelligence;2024-08-07
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