Video Panoptic Segmentation On Kitti Step
Métriques
AQ
SQ
STQ
Résultats
Résultats de performance de divers modèles sur ce benchmark
Nom du modèle | AQ | SQ | STQ | Paper Title | Repository |
|---|---|---|---|---|---|
| TarViS (Swin-T) | 71.2 | 69.9 | 70.6 | TarViS: A Unified Approach for Target-based Video Segmentation | |
| Video K-Net (Swin-L) | 73.0 | 75.0 | 74.0 | Video K-Net: A Simple, Strong, and Unified Baseline for Video Segmentation | |
| Unified Perception | 56.4 | 61.9 | 59.1 | Unified Perception: Efficient Depth-Aware Video Panoptic Segmentation with Minimal Annotation Costs | - |
| TarViS (Swin-L) | 72.0 | 72.0 | 73.0 | TarViS: A Unified Approach for Target-based Video Segmentation | |
| TarViS (ResNet-50) | 70.3 | 68.8 | 69.6 | TarViS: A Unified Approach for Target-based Video Segmentation | |
| Tube-Link(Swin-base) | 69.0 | 74.0 | 72.0 | Tube-Link: A Flexible Cross Tube Framework for Universal Video Segmentation |
0 of 6 row(s) selected.