Papers › DG-STGCN: Dynamic Spatial-Temporal Modeling for Skeleton-based Action Recognition
DG-STGCN: Dynamic Spatial-Temporal Modeling for Skeleton-based Action Recognition
Haodong Duan, Jiaqi Wang, Kai Chen, Dahua Lin
Graph convolution networks (GCN) have been widely used in skeleton-based action recognition. We note that existing GCN-based approaches primarily rely on prescribed graphical structures (ie., a manually defined topology of skeleton joints), which limits their flexibility to capture complicated correlations between joints. To move beyond this limitation, we propose a new framework for skeleton-based action recognition, namely Dynamic Group Spatio-Temporal GCN (DG-STGCN). It consists of two modules, DG-GCN and DG-TCN, respectively, for spatial and temporal modeling. In particular, DG-GCN uses learned affinity matrices to capture dynamic graphical structures instead of relying on a prescribed one, while DG-TCN performs group-wise temporal convolutions with varying receptive fields and incorporates a dynamic joint-skeleton fusion module for adaptive multi-level temporal modeling. On a wide range of benchmarks, including NTURGB+D, Kinetics-Skeleton, BABEL, and Toyota SmartHome, DG-STGCN consistently outperforms state-of-the-art methods, often by a notable margin.
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Code
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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 | NTU RGB+D | DG-STGCN | Accuracy (CS) | 93.2 | #15 of 135 | Archive leaderboard | report |
| Skeleton Based Action Recognition | NTU RGB+D | DG-STGCN | Accuracy (CV) | 97.5 | #15 of 135 | Archive leaderboard | report |
| Skeleton Based Action Recognition | NTU RGB+D | DG-STGCN | Ensembled Modalities | 4 | #15 of 135 | Archive leaderboard | report |
| Skeleton Based Action Recognition | NTU RGB+D 120 | DG-STGCN | Accuracy (Cross-Setup) | 91.3 | #17 of 83 | Archive leaderboard | report |
| Skeleton Based Action Recognition | NTU RGB+D 120 | DG-STGCN | Accuracy (Cross-Subject) | 89.6 | #17 of 83 | Archive leaderboard | report |
| Skeleton Based Action Recognition | NTU RGB+D 120 | DG-STGCN | Ensembled Modalities | 4 | #17 of 83 | Archive leaderboard | report |
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Methods
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