Scene Recognition On Yup
Métriques
Accuracy (%)
Résultats
Résultats de performance de divers modèles sur ce benchmark
Nom du modèle | Accuracy (%) | Paper Title | Repository |
|---|---|---|---|
| DEEP-HAL with ODF+SDF (I3D) | 94.4 | Self-supervising Action Recognition by Statistical Moment and Subspace Descriptors | - |
| TO+MaxExp+IDT | 93.1 | High-order Tensor Pooling with Attention for Action Recognition | - |
| HalluciNet (ResNet-50) | 84.44 | HalluciNet-ing Spatiotemporal Representations Using a 2D-CNN | - |
| HAF+BoW/FV halluc. | 92.6 | Hallucinating IDT Descriptors and I3D Optical Flow Features for Action Recognition with CNNs | - |
| SO+MaxExp+IDT | 92.5 | High-order Tensor Pooling with Attention for Action Recognition | - |
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