Unsupervised Image Matching and Object Discovery as Optimization

Huy V. Vo, Francis Bach, Minsu Cho, Kai Han, Yann LeCun, Patrick Perez, Jean Ponce; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 8287-8296

Abstract


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.

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[bibtex]
@InProceedings{Vo_2019_CVPR,
author = {Vo, Huy V. and Bach, Francis and Cho, Minsu and Han, Kai and LeCun, Yann and Perez, Patrick and Ponce, Jean},
title = {Unsupervised Image Matching and Object Discovery as Optimization},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}