Papers › Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition
Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition
Pengfei Zhang, Cuiling Lan, Wen-Jun Zeng, Junliang Xing, Jianru Xue, Nanning Zheng
Skeleton-based human action recognition has attracted great interest thanks to the easy accessibility of the human skeleton data. Recently, there is a trend of using very deep feedforward neural networks to model the 3D coordinates of joints without considering the computational efficiency. In this paper, we propose a simple yet effective semantics-guided neural network (SGN) for skeleton-based action recognition. We explicitly introduce the high level semantics of joints (joint type and frame index) into the network to enhance the feature representation capability. In addition, we exploit the relationship of joints hierarchically through two modules, i.e., a joint-level module for modeling the correlations of joints in the same frame and a framelevel module for modeling the dependencies of frames by taking the joints in the same frame as a whole. A strong baseline is proposed to facilitate the study of this field. With an order of magnitude smaller model size than most previous works, SGN achieves the state-of-the-art performance on the NTU60, NTU120, and SYSU datasets. The source code is available at https://github.com/microsoft/SGN.
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Code
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Skeleton Based Action Recognition | N-UCLA | SGN | Accuracy | 92.5% | #21 of 25 | Archive leaderboard | report |
| Skeleton Based Action Recognition | NTU RGB+D | SGN | Accuracy (CS) | 89.0 | #67 of 135 | Archive leaderboard | report |
| Skeleton Based Action Recognition | NTU RGB+D | SGN | Accuracy (CV) | 94.5 | #67 of 135 | Archive leaderboard | report |
| Skeleton Based Action Recognition | SYSU 3D | SGN | Accuracy | 86.9% | #1 of 9 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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