Papers › Dense Unsupervised Learning for Video Segmentation
Dense Unsupervised Learning for Video Segmentation
Nikita Araslanov, Simone Schaub-Meyer, Stefan Roth
We present a novel approach to unsupervised learning for video object segmentation (VOS). Unlike previous work, our formulation allows to learn dense feature representations directly in a fully convolutional regime. We rely on uniform grid sampling to extract a set of anchors and train our model to disambiguate between them on both inter- and intra-video levels. However, a naive scheme to train such a model results in a degenerate solution. We propose to prevent this with a simple regularisation scheme, accommodating the equivariance property of the segmentation task to similarity transformations. Our training objective admits efficient implementation and exhibits fast training convergence. On established VOS benchmarks, our approach exceeds the segmentation accuracy of previous work despite using significantly less training data and compute power.
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Code
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Semi-Supervised Video Object Segmentation | DAVIS 2017 (val) | Araslanov et al. | F-measure (Mean) | 71.7 | #62 of 81 | Archive leaderboard | report |
| Semi-Supervised Video Object Segmentation | DAVIS 2017 (val) | Araslanov et al. | F-measure (Recall) | 84.8 | #62 of 81 | Archive leaderboard | report |
| Semi-Supervised Video Object Segmentation | DAVIS 2017 (val) | Araslanov et al. | J&F | 69.4 | #62 of 81 | Archive leaderboard | report |
| Semi-Supervised Video Object Segmentation | DAVIS 2017 (val) | Araslanov et al. | Jaccard (Mean) | 67.1 | #62 of 81 | Archive leaderboard | report |
| Semi-Supervised Video Object Segmentation | DAVIS 2017 (val) | Araslanov et al. | Jaccard (Recall) | 80.9 | #62 of 81 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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