Unsupervised Image Matching and Object Discovery as Optimization
| 
94 | 
43 | 
0
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 specifific applications, unsupervised learning has emerged as an important fifield 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
还没有评论，说两句吧！
热门资源
Learning to Predi...
Much of model-based reinforcement learning invo...
Stratified Strate...
In this paper we introduce Stratified Strategy ...
The Variational S...
Unlike traditional images which do not offer in...
A Mathematical Mo...
Direct democracy, where each voter casts one vo...
Rating-Boosted La...
The performance of a recommendation system reli...
智能在线
400-630-6780
聆听.建议反馈
E-mail: support@tusaishared.com
资源分类
