| Dataset Name | SOTA Method | Metric | Trend | ||
|---|---|---|---|---|---|
| FineGym290 | Representation Learning via Global Temporal Alignment and Cycle-Consistency | Accuracy46.54 | 12 | 4d ago | |
| FineGym101 | Representation Learning via Global Temporal Alignment and Cycle-Consistency | Accuracy49.51 | 12 | 4d ago | |
| Gym99-skeleton severe temporal corruption (Sev.) | FineTec | Top-1 Acc89.1 | 9 | 4d ago | |
| Gym99-skeleton (moderate temporal corruption (Mod.)) | FineTec | Top-1 Acc90.6 | 9 | 4d ago | |
| Gym99-skeleton minor temporal corruption (Min.) | FineTec | Top-1 Acc92.1 | 9 | 4d ago | |
| Gym288-skeleton (severe temporal corruption) | FineTec | Top-1 Acc78.1 | 9 | 4d ago | |
| Gym288-skeleton moderate temporal corruption (Mod.) | FineTec | Top-1 Acc79.7 | 9 | 4d ago | |
| Gym288-skeleton minor temporal corruption (Min.) | FineTec | Top-1 Acc81.5 | 9 | 4d ago | |
| FineGym 288 (original clips) | Ours | Accuracy24.16 | 6 | 4d ago | |
| FineGym99 (original clips) | Ours | Accuracy27.81 | 6 | 4d ago | |
| MTL-AQA | NS-AQA | Accuracy (Armstand)99.79 | 4 | 4d ago |
