Self Supervised Human Action Recognition On
Metrics
Classifier
Encoder
xset (%)
xsub (%)
Results
Performance results of various models on this benchmark
Model Name | Classifier | Encoder | xset (%) | xsub (%) | Paper Title | Repository |
|---|---|---|---|---|---|---|
| AS-CAL | FC | LSTM | 49.2 | 48.6 | Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition | - |
| 3s-AimCLR | FC | ST-GCN | 68.8 | 68.2 | Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition | - |
| 3s-RVTCLR+ | FC | ST-GCN | 68.9 | 68.0 | Modeling the Relative Visual Tempo for Self-supervised Skeleton-based Action Recognition | |
| PCRP | FC | GRU | 45.1 | 41.7 | Prototypical Contrast and Reverse Prediction: Unsupervised Skeleton Based Action Recognition | - |
| P&C | FC | GRU | 42.7 | 41.7 | PREDICT & CLUSTER: Unsupervised Skeleton Based Action Recognition | - |
| MCAE | FC | MCAE | 54.7 | 52.8 | Unsupervised Motion Representation Learning with Capsule Autoencoders | - |
| CMCS | FC | ST-GCN | 71.7 | 68.5 | Cross-Model Cross-Stream Learning for Self-Supervised Human Action Recognition | |
| SDS-CL | FC | DSTA | 55.6 | 50.6 | Spatiotemporal Decouple-and-Squeeze Contrastive Learning for Semi-Supervised Skeleton-based Action Recognition | - |
0 of 8 row(s) selected.