Name
Abstract | ||
|---|---|---|
Learning with complete or partial supervision is powerful 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, unsupervised learning comes in various guises. We focus here on the unsupervised discovery and matching of object categories 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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/CVPR.2019.00848 | 2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019) |
Field | DocType | Volume |
Annotation,Computer science,Image matching,Unsupervised learning,Artificial intelligence,Optimization problem,Machine learning | Journal | abs/1904.03148 |
ISSN | Citations | PageRank |
1063-6919 | 2 | 0.35 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Huy V. Vo | 1 | 2 | 0.35 |
| Francis Bach | 2 | 11490 | 622.29 |
| Minsu Cho | 3 | 677 | 35.74 |
| Kai Han | 4 | 10 | 7.90 |
| Yann LeCun | 5 | 26090 | 3771.21 |
| Patrick Pérez | 6 | 6529 | 391.34 |
| Jean Ponce | 7 | 12182 | 902.31 |