Adding Unlabeled Samples to Categories by Learned Attributes

Jonghyun Choi, Mohammad Rastegari, Ali Farhadi, Larry S. Davis; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013, pp. 875-882

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, ImageNet.

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[bibtex]
@InProceedings{Choi_2013_CVPR,
author = {Choi, Jonghyun and Rastegari, Mohammad and Farhadi, Ali and Davis, Larry S.},
title = {Adding Unlabeled Samples to Categories by Learned Attributes},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2013}
}