Papers › Modeling the Relative Visual Tempo for Self-supervised Skeleton-based Action Recognition
Modeling the Relative Visual Tempo for Self-supervised Skeleton-based Action Recognition
Yisheng Zhu, Hu Han, Zhengtao Yu, Guangcan Liu
Visual tempo characterizes the dynamics and the temporal evolution, which helps describe actions. Recent approaches directly perform visual tempo prediction on skeleton sequences, which may suffer from insufficient feature representation issue. In this paper, we observe that relative visual tempo is more in line with human intuition, and thus providing more effective supervision signals. Based on this, we propose a novel Relative Visual Tempo Contrastive Learning framework for skeleton action Representation (RVTCLR). Specifically, we design a Relative Visual Tempo Learning (RVTL) task to explore the motion information in intra-video clips, and an Appearance-Consistency (AC) task to learn appearance information simultaneously, resulting in more representative spatiotemporal features. Furthermore, skeleton sequence data is much sparser than RGB data, making the network learn shortcuts, and overfit to low-level information such as skeleton scales. To learn high-order semantics, we further design a new Distribution-Consistency (DC) branch, containing three components: Skeleton-specific Data Augmentation (SDA), Fine-grained Skeleton Encoding Module (FSEM), and Distribution-aware Diversity (DD) Loss. We term our entire method (RVTCLR with DC) as RVTCLR+. Extensive experiments on NTU RGB+D 60 and NTU RGB+D 120 datasets demonstrate that our RVTCLR+ can achieve competitive results over the state-of-the-art methods. Code is available at https://github.com/Zhuysheng/RVTCLR.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Self-Supervised Human Action Recognition | NTU RGB+D 120 | 3s-RVTCLR+ | Classifier | FC | #3 of 8 | Archive leaderboard | report |
| Self-Supervised Human Action Recognition | NTU RGB+D 120 | 3s-RVTCLR+ | Encoder | ST-GCN | #3 of 8 | Archive leaderboard | report |
| Self-Supervised Human Action Recognition | NTU RGB+D 120 | 3s-RVTCLR+ | xset (%) | 68.9 | #3 of 8 | Archive leaderboard | report |
| Self-Supervised Human Action Recognition | NTU RGB+D 120 | 3s-RVTCLR+ | xsub (%) | 68.0 | #3 of 8 | 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.
Methods
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