Abstract
We propose a method to expand the visual coverage of training sets that consist of a small number of labeled examples using learned attributes. Our optimization formulation discovers category specific attributes as well as the images that have high confidence in terms of the attributes. In addition, we propose a method to stably capture example-specific attributes for a small sized training set. Our method adds images to a category from a large unlabeled image pool, and leads to significant improvement in category recognition accuracy evaluated on a large-scale dataset, Image Net.
Original language | English |
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Article number | 6618962 |
Pages (from-to) | 875-882 |
Number of pages | 8 |
Journal | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
DOIs | |
Publication status | Published - 2013 |
Event | 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2013 - Portland, OR, United States Duration: 2013 Jun 23 → 2013 Jun 28 |
All Science Journal Classification (ASJC) codes
- Software
- Computer Vision and Pattern Recognition