Motion-Refined DINOSAUR for Unsupervised Multi-Object Discovery
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866916930245361664 |
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| author | Gong, Xinrui Hahn, Oliver Reich, Christoph Singh, Krishnakant Schaub-Meyer, Simone Cremers, Daniel Roth, Stefan |
| author_facet | Gong, Xinrui Hahn, Oliver Reich, Christoph Singh, Krishnakant Schaub-Meyer, Simone Cremers, Daniel Roth, Stefan |
| contents | Unsupervised multi-object discovery (MOD) aims to detect and localize distinct object instances in visual scenes without any form of human supervision. Recent approaches leverage object-centric learning (OCL) and motion cues from video to identify individual objects. However, these approaches use supervision to generate pseudo labels to train the OCL model. We address this limitation with MR-DINOSAUR -- Motion-Refined DINOSAUR -- a minimalistic unsupervised approach that extends the self-supervised pre-trained OCL model, DINOSAUR, to the task of unsupervised multi-object discovery. We generate high-quality unsupervised pseudo labels by retrieving video frames without camera motion for which we perform motion segmentation of unsupervised optical flow. We refine DINOSAUR's slot representations using these pseudo labels and train a slot deactivation module to assign slots to foreground and background. Despite its conceptual simplicity, MR-DINOSAUR achieves strong multi-object discovery results on the TRI-PD and KITTI datasets, outperforming the previous state of the art despite being fully unsupervised. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_02545 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Motion-Refined DINOSAUR for Unsupervised Multi-Object Discovery Gong, Xinrui Hahn, Oliver Reich, Christoph Singh, Krishnakant Schaub-Meyer, Simone Cremers, Daniel Roth, Stefan Computer Vision and Pattern Recognition Unsupervised multi-object discovery (MOD) aims to detect and localize distinct object instances in visual scenes without any form of human supervision. Recent approaches leverage object-centric learning (OCL) and motion cues from video to identify individual objects. However, these approaches use supervision to generate pseudo labels to train the OCL model. We address this limitation with MR-DINOSAUR -- Motion-Refined DINOSAUR -- a minimalistic unsupervised approach that extends the self-supervised pre-trained OCL model, DINOSAUR, to the task of unsupervised multi-object discovery. We generate high-quality unsupervised pseudo labels by retrieving video frames without camera motion for which we perform motion segmentation of unsupervised optical flow. We refine DINOSAUR's slot representations using these pseudo labels and train a slot deactivation module to assign slots to foreground and background. Despite its conceptual simplicity, MR-DINOSAUR achieves strong multi-object discovery results on the TRI-PD and KITTI datasets, outperforming the previous state of the art despite being fully unsupervised. |
| title | Motion-Refined DINOSAUR for Unsupervised Multi-Object Discovery |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.02545 |