ML Anthology

Unsupervised Image Matching and Object Discovery as Optimization

Huy V. Vo, Francis Bach, Minsu Cho, Kai Han, Yann LeCun, Patrick Perez, Jean Ponce
CVPR 2019
doi:10.1109/CVPR.2019.00848 /cvpr/2019/vo2019cvpr-unsupervised/

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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Text

Vo et al. "Unsupervised Image Matching and Object Discovery as Optimization." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019. doi:10.1109/CVPR.2019.00848

Markdown

[Vo et al. "Unsupervised Image Matching and Object Discovery as Optimization." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019.](https://mlanthology.org/cvpr/2019/vo2019cvpr-unsupervised/) doi:10.1109/CVPR.2019.00848

BibTeX

@inproceedings{vo2019cvpr-unsupervised,
  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 Perez, Patrick and Ponce, Jean},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2019},
  doi       = {10.1109/CVPR.2019.00848},
  url       = {https://mlanthology.org/cvpr/2019/vo2019cvpr-unsupervised/}
}