Papers › Understanding Self-Supervised Features for Learning Unsupervised Instance Segmentation
Understanding Self-Supervised Features for Learning Unsupervised Instance Segmentation
Paul Engstler, Luke Melas-Kyriazi, Christian Rupprecht, Iro Laina
Self-supervised learning (SSL) can be used to solve complex visual tasks without human labels. Self-supervised representations encode useful semantic information about images, and as a result, they have already been used for tasks such as unsupervised semantic segmentation. In this paper, we investigate self-supervised representations for instance segmentation without any manual annotations. We find that the features of different SSL methods vary in their level of instance-awareness. In particular, DINO features, which are known to be excellent semantic descriptors, lack behind MAE features in their sensitivity for separating instances.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Unsupervised Instance Segmentation | COCO val2017 | Self-Training (MAE) | AP | 5.2 | #3 of 5 | Archive leaderboard | report |
| Unsupervised Instance Segmentation | COCO val2017 | Self-Training (MAE) | AP50 | 12.1 | #3 of 5 | Archive leaderboard | report |
| Unsupervised Instance Segmentation | COCO val2017 | Self-Training (MAE) | AP75 | 3.7 | #3 of 5 | Archive leaderboard | report |
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Methods
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