Graph-based Action Recognition
Graph-Based Action Recognition is a methodological framework within computer vision that represents human actions as spatiotemporal graphs, where nodes denote body joints or parts and edges encode their spatial and temporal dependencies, to model articulated human motion for action classification.
282
Publications
33.6K
Citations
936
Authors
304
Institutions
Publications per year
2017–2026
227
Parents
282
Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition
Lei Shi, Yifan Zhang, Jian Cheng et al. · 2019 · 2K citations
Action recognition based on a bag of 3D points
Wanqing Li, Zhengyou Zhang, Zicheng Liu · 2010 · 1.4K citations
Actional-Structural Graph Convolutional Networks for Skeleton-Based Action Recognition
Maosen Li, Siheng Chen, Xu Chen et al. · 2019 · 1.2K citations
Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition
Ziyu Liu, Hongwen Zhang, Zhenghao Chen et al. · 2020 · 1.2K citations
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Chinese Academy of Sciences
Beijing, China
21 h-index
81 publications
30.6K citations
University of Chinese Academy of Sciences
Beijing, China
20 h-index
60 publications
30.4K citations

Xi'an Jiaotong University
Xi'an, China
12 h-index
30 publications
1.1K citations
Institute of Automation
Beijing, China
11 h-index
35 publications
7.5K citations
Tsinghua University
Beijing, China
9 h-index
26 publications
2.2K citations

South China University of Technology
Guangzhou, China
9 h-index
26 publications
1.5K citations

Shanghai Jiao Tong University
Shanghai, China
8 h-index
22 publications
6.6K citations

Centre National de la Recherche Scientifique
Paris, France
8 h-index
16 publications
988 citations
1–8 of 304
Repository · 30 publications · 1.8K citations
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14 publications · 652 citations
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12 publications · 849 citations
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10 publications · 1.2K citations
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9 publications · 276 citations
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