Unsupervised Image Matching and Object Discovery as Optimization

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

CVPR 2019

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}
}