Papers › Unsupervised Multi-object Segmentation Using Attention and Soft-argmax
Unsupervised Multi-object Segmentation Using Attention and Soft-argmax
Bruno Sauvalle, Arnaud de La Fortelle
We introduce a new architecture for unsupervised object-centric representation learning and multi-object detection and segmentation, which uses a translation-equivariant attention mechanism to predict the coordinates of the objects present in the scene and to associate a feature vector to each object. A transformer encoder handles occlusions and redundant detections, and a convolutional autoencoder is in charge of background reconstruction. We show that this architecture significantly outperforms the state of the art on complex synthetic benchmarks.
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Code
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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 | ClevrTex | AST-Seg-B3-CT | MSE | 139±7 | #1 of 12 | Archive leaderboard | report |
| Unsupervised Object Segmentation | ClevrTex | AST-Seg-B3-CT | mIoU | 79.58±0.54 | #1 of 12 | Archive leaderboard | report |
| Unsupervised Object Segmentation | ClevrTex | AST | MSE | 167± 1 | #2 of 12 | Archive leaderboard | report |
| Unsupervised Object Segmentation | ClevrTex | AST | mIoU | 66.62± 0.80 | #2 of 12 | Archive leaderboard | report |
| Unsupervised Object Segmentation | ObjectsRoom | AST | ARI-FG | 0.87 | #1 of 5 | Archive leaderboard | report |
| Unsupervised Object Segmentation | ShapeStacks | AST | ARI-FG | 0.82 | #1 of 5 | 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.
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