Papers › Vertex Feature Encoding and Hierarchical Temporal Modeling in a Spatial-Temporal Graph...
Vertex Feature Encoding and Hierarchical Temporal Modeling in a Spatial-Temporal Graph Convolutional Network for Action Recognition
Konstantinos Papadopoulos, Enjie Ghorbel, Djamila Aouada, Björn Ottersten
This paper extends the Spatial-Temporal Graph Convolutional Network (ST-GCN) for skeleton-based action recognition by introducing two novel modules, namely, the Graph Vertex Feature Encoder (GVFE) and the Dilated Hierarchical Temporal Convolutional Network (DH-TCN). On the one hand, the GVFE module learns appropriate vertex features for action recognition by encoding raw skeleton data into a new feature space. On the other hand, the DH-TCN module is capable of capturing both short-term and long-term temporal dependencies using a hierarchical dilated convolutional network. Experiments have been conducted on the challenging NTU RGB-D-60 and NTU RGB-D 120 datasets. The obtained results show that our method competes with state-of-the-art approaches while using a smaller number of layers and parameters; thus reducing the required training time and memory.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Action Recognition | NTU RGB+D 120 | ST-GCN + AS-GCN w/DH-TCN | Accuracy (Cross-Setup) | 78.3 | #17 of 21 | Archive leaderboard | report |
| Action Recognition | NTU RGB+D 120 | ST-GCN + AS-GCN w/DH-TCN | Accuracy (Cross-Subject) | 79.2 | #17 of 21 | Archive leaderboard | report |
| Skeleton Based Action Recognition | NTU RGB+D | GVFE + AS-GCN with DH-TCN | Accuracy (CS) | 85.3 | #93 of 135 | Archive leaderboard | report |
| Skeleton Based Action Recognition | NTU RGB+D | GVFE + AS-GCN with DH-TCN | Accuracy (CV) | 92.8 | #93 of 135 | Archive leaderboard | report |
| Skeleton Based Action Recognition | NTU RGB+D 120 | GVFE + AS-GCN with DH-TCN | Accuracy (Cross-Setup) | 79.8% | #64 of 83 | Archive leaderboard | report |
| Skeleton Based Action Recognition | NTU RGB+D 120 | GVFE + AS-GCN with DH-TCN | Accuracy (Cross-Subject) | 78.3% | #64 of 83 | 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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