Unsupervised Object Segmentation On Fbms 59
Métriques
mIoU
Résultats
Résultats de performance de divers modèles sur ce benchmark
Nom du modèle | mIoU | Paper Title | Repository |
|---|---|---|---|
| RCF (with post-processing) | 72.4 | Bootstrapping Objectness from Videos by Relaxed Common Fate and Visual Grouping | |
| MOD | 61.3 | Motion-inductive Self-supervised Object Discovery in Videos | - |
| OCLR | 65.4 | Segmenting Moving Objects via an Object-Centric Layered Representation | |
| GWM | 60.9 | Guess What Moves: Unsupervised Video and Image Segmentation by Anticipating Motion | - |
| AMD | 47.5 | The Emergence of Objectness: Learning Zero-Shot Segmentation from Videos | |
| RCF (without post-processing) | 69.9 | Bootstrapping Objectness from Videos by Relaxed Common Fate and Visual Grouping | |
| TokenCut | 60.2 | TokenCut: Segmenting Objects in Images and Videos with Self-supervised Transformer and Normalized Cut | - |
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