Unsupervised Image Matching and Object Discovery as Optimization

By Huy V. Vo, Francis R. Bach, Minsu Cho, Kai Han, Yann LeCun, Patrick Pérez, Jean Ponce. 2019

69 citations recorded by the source corpus.

Learning with complete or partial supervision is power- ful 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, unsu- pervised learning comes in various guises. We focus here on the unsupervised discovery and matching of object cate- gories among images in a collection, following the work of Cho et al. [12]. 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.

Subfields: Computer Vision and Pattern Recognition, Mathematical Optimization

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Citations in the corpus

Self-labelling via simultaneous clustering and representation learning (2019, background): “Finally, (Bach & Harchaoui, 2008; Vo et al., 2019) use convex relaxations to regularized afﬁne-transformation invariant linear clustering, that does not scale to larger datasets.”
Toward unsupervised, multi-object discovery in large-scale image collections (2020, methodology): “We address the limitation of using off-the-shelf region proposals of [34] with insights gained from the remarkably effective method for image colocalization proposed by Wei et al.”