Adding Unlabeled Samples to Categories by Learned Attributes

Jonghyun Choi, Mohammad Rastegari, Ali Farhadi, Larry S. Davis

CVPR 2013

doi:10.1109/CVPR.2013.118 /cvpr/2013/choi2013cvpr-adding/

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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Cite

Text

Choi et al. "Adding Unlabeled Samples to Categories by Learned Attributes." Conference on Computer Vision and Pattern Recognition, 2013. doi:10.1109/CVPR.2013.118

Markdown

[Choi et al. "Adding Unlabeled Samples to Categories by Learned Attributes." Conference on Computer Vision and Pattern Recognition, 2013.](https://mlanthology.org/cvpr/2013/choi2013cvpr-adding/) doi:10.1109/CVPR.2013.118

BibTeX

@inproceedings{choi2013cvpr-adding,
  title     = {{Adding Unlabeled Samples to Categories by Learned Attributes}},
  author    = {Choi, Jonghyun and Rastegari, Mohammad and Farhadi, Ali and Davis, Larry S.},
  booktitle = {Conference on Computer Vision and Pattern Recognition},
  year      = {2013},
  doi       = {10.1109/CVPR.2013.118},
  url       = {https://mlanthology.org/cvpr/2013/choi2013cvpr-adding/}
}