Papers › CHASE: Learning Convex Hull Adaptive Shift for Skeleton-based Multi-Entity Action Recognition
CHASE: Learning Convex Hull Adaptive Shift for Skeleton-based Multi-Entity Action Recognition
Yuhang Wen, Mengyuan Liu, Songtao Wu, Beichen Ding
Skeleton-based multi-entity action recognition is a challenging task aiming to identify interactive actions or group activities involving multiple diverse entities. Existing models for individuals often fall short in this task due to the inherent distribution discrepancies among entity skeletons, leading to suboptimal backbone optimization. To this end, we introduce a Convex Hull Adaptive Shift based multi-Entity action recognition method (CHASE), which mitigates inter-entity distribution gaps and unbiases subsequent backbones. Specifically, CHASE comprises a learnable parameterized network and an auxiliary objective. The parameterized network achieves plausible, sample-adaptive repositioning of skeleton sequences through two key components. First, the Implicit Convex Hull Constrained Adaptive Shift ensures that the new origin of the coordinate system is within the skeleton convex hull. Second, the Coefficient Learning Block provides a lightweight parameterization of the mapping from skeleton sequences to their specific coefficients in convex combinations. Moreover, to guide the optimization of this network for discrepancy minimization, we propose the Mini-batch Pair-wise Maximum Mean Discrepancy as the additional objective. CHASE operates as a sample-adaptive normalization method to mitigate inter-entity distribution discrepancies, thereby reducing data bias and improving the subsequent classifier's multi-entity action recognition performance. Extensive experiments on six datasets, including NTU Mutual 11/26, H2O, Assembly101, Collective Activity and Volleyball, consistently verify our approach by seamlessly adapting to single-entity backbones and boosting their performance in multi-entity scenarios. Our code is publicly available at https://github.com/Necolizer/CHASE .
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 3D Action Recognition | Assembly101 | CHASE(CTR-GCN) | Actions Top-1 | 28.03 | #6 of 7 | Archive leaderboard | report |
| Group Activity Recognition | Collective Activity | CHASE(CTR-GCN) | Accuracy | 89.61 | #4 of 6 | Archive leaderboard | report |
| Group Activity Recognition | Volleyball | CHASE(CTR-GCN) | Accuracy | 92.89 | #7 of 12 | Archive leaderboard | report |
| Human Interaction Recognition | NTU RGB+D | CHASE(CTR-GCN) | Accuracy (Cross-Subject) | 96.5 | #2 of 5 | Archive leaderboard | report |
| Human Interaction Recognition | NTU RGB+D | CHASE(CTR-GCN) | Accuracy (Cross-View) | 98.8 | #2 of 5 | Archive leaderboard | report |
| Human Interaction Recognition | NTU RGB+D 120 | CHASE(CTR-GCN) | Accuracy (Cross-Setup) | 92.3 | #2 of 6 | Archive leaderboard | report |
| Human Interaction Recognition | NTU RGB+D 120 | CHASE(CTR-GCN) | Accuracy (Cross-Subject) | 91.3 | #2 of 6 | Archive leaderboard | report |
| Skeleton Based Action Recognition | H2O (2 Hands and Objects) | CHASE(STSA-Net) | Accuracy | 94.77 | #1 of 4 | 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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