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Skeleton Based Action Recognition On Varying
Métriques
Accuracy (AV I)
Accuracy (AV II)
Accuracy (CS)
Accuracy (CV I)
Accuracy (CV II)
Résultats
Résultats de performance de divers modèles sur ce benchmark
| Paper Title | ||||||
|---|---|---|---|---|---|---|
| VS-CNN | 57% | 75% | 76% | 29% | 71% | A Large-scale Varying-view RGB-D Action Dataset for Arbitrary-view Human Action Recognition |
| ST-GCN | 53% | 43% | 71% | 25% | 56% | Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition |
| Res-TCN | 48% | 68% | 63% | 14% | 48% | Interpretable 3D Human Action Analysis with Temporal Convolutional Networks |
| SK-CNN | 43% | 77% | 59% | 26% | 68% | Enhanced skeleton visualization for view invariant human action recognition |
| TCN | 43% | 64% | 56% | 16% | 43% | Temporal Convolutional Networks for Action Segmentation and Detection |
| P-LSTM | 33% | 50% | 60% | 13% | 33% | NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis |
| LSTM | 31% | 68% | 56% | 16% | 31% | NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis |
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