Adding unlabeled samples to categories by learned attributes

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

Research output: Contribution to journalConference articlepeer-review

25 Citations (Scopus)

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 languageEnglish
Article number6618962
Pages (from-to)875-882
Number of pages8
JournalProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
DOIs
Publication statusPublished - 2013
Event26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2013 - Portland, OR, United States
Duration: 2013 Jun 232013 Jun 28

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition

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