Papers › Unsupervised Image Matching and Object Discovery as Optimization
Unsupervised Image Matching and Object Discovery as Optimization
Huy V. Vo, Francis Bach, Minsu Cho, Kai Han, Yann Lecun, Patrick Perez, Jean Ponce
Learning with complete or partial supervision is powerful but relies on ever-growing human annotation efforts. As a way to mitigate this serious problem, as well as to serve specific applications, unsupervised learning has emerged as an important field of research. In computer vision, unsupervised learning comes in various guises. We focus here on the unsupervised discovery and matching of object categories among images in a collection, following the work of Cho et al. 2015. We show that the original approach can be reformulated and solved as a proper optimization problem. Experiments on several benchmarks establish the merit of our approach.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Single-object discovery | Object Discovery | OSD | CorLoc | 83 | #2 of 2 | Archive leaderboard | report |
| Single-object discovery | VOC_6x2 | OSD | CorLoc | 60.2 | #2 of 2 | Archive leaderboard | report |
| Single-object discovery | VOC_all | OSD | CorLoc | 39.8 | #3 of 3 | Archive leaderboard | report |
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