Adding Unlabeled Samples to Categories by Learned Attributes


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.

DOI: 10.1109/CVPR.2013.118

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@article{Choi2013AddingUS, title={Adding Unlabeled Samples to Categories by Learned Attributes}, author={Jonghyun Choi and Mohammad Rastegari and Ali Farhadi and Larry S. Davis}, journal={2013 IEEE Conference on Computer Vision and Pattern Recognition}, year={2013}, pages={875-882} }