Unsupervised Image Matching and Object Discovery as Optimization

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.

BibTeX

@inproceedings{Vo19UOD,
title     = {Unsupervised image matching and object discovery as optimization},
author    = {Vo, Huy V. and Bach, Francis and Cho, Minsu and Han, Kai and LeCun, Yann and P{\'e}rez, Patrick and Ponce, Jean},
booktitle = {CVPR},
year      = {2019}
}