Papers › Guess What Moves: Unsupervised Video and Image Segmentation by Anticipating Motion
Guess What Moves: Unsupervised Video and Image Segmentation by Anticipating Motion
Subhabrata Choudhury, Laurynas Karazija, Iro Laina, Andrea Vedaldi, Christian Rupprecht
Motion, measured via optical flow, provides a powerful cue to discover and learn objects in images and videos. However, compared to using appearance, it has some blind spots, such as the fact that objects become invisible if they do not move. In this work, we propose an approach that combines the strengths of motion-based and appearance-based segmentation. We propose to supervise an image segmentation network with the pretext task of predicting regions that are likely to contain simple motion patterns, and thus likely to correspond to objects. As the model only uses a single image as input, we can apply it in two settings: unsupervised video segmentation, and unsupervised image segmentation. We achieve state-of-the-art results for videos, and demonstrate the viability of our approach on still images containing novel objects. Additionally we experiment with different motion models and optical flow backbones and find the method to be robust to these change. Project page and code available at https://www.robots.ox.ac.uk/~vgg/research/gwm.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Unsupervised Object Segmentation | DAVIS 2016 | GWM | J score | 71.2 | #5 of 9 | Archive leaderboard | report |
| Unsupervised Object Segmentation | FBMS-59 | GWM | mIoU | 60.9 | #5 of 7 | Archive leaderboard | report |
| Unsupervised Object Segmentation | SegTrack-v2 | GWM | mIoU | 66.7 | #4 of 8 | Archive leaderboard | report |
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