Unsupervised Object Segmentation On Davis
Metrics
J score
Results
Performance results of various models on this benchmark
Model Name | J score | Paper Title | Repository |
|---|---|---|---|
| MG | 68.3 | Self-supervised Video Object Segmentation by Motion Grouping | - |
| RCF (with Post-Processing) | 83.0 | Bootstrapping Objectness from Videos by Relaxed Common Fate and Visual Grouping | - |
| EM | 69.8 | EM-driven unsupervised learning for efficient motion segmentation | - |
| OCLR | 72.1 | Segmenting Moving Objects via an Object-Centric Layered Representation | - |
| RCF (without Post-Processing) | 80.9 | Bootstrapping Objectness from Videos by Relaxed Common Fate and Visual Grouping | - |
| SIMO | 67.8 | - | - |
| MOD | 73.9 | Motion-inductive Self-supervised Object Discovery in Videos | - |
| GWM | 71.2 | Guess What Moves: Unsupervised Video and Image Segmentation by Anticipating Motion | - |
| AMD | 57.8 | The Emergence of Objectness: Learning Zero-Shot Segmentation from Videos | - |
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