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Attribute relaxation from class level to instance level for zero-shot learning

Zhang, Haofeng; Long, Yang; Zhao, Chunxia

Authors

Haofeng Zhang

Chunxia Zhao



Abstract

Conventional zero-shot learning (ZSL) methods usually use class-level attribute, which corresponds to a batch of images of same category. This setting is not reasonable since the images even though belong to same category still have variances in their attribute items. To alleviate this phenomenon, the authors propose a novel method namely attribute relaxation (AR) to extend attributes from class level to instance level by adding a small variance matrix, which is more reasonable than traditional ZSL methods such as Semantic AutoEncoder that projects features from multi to one. Extensive experiments on four popular datasets show that AR can significantly improve the method using only class-level attributes, and verifies that AR can make the projected features in attribute space more discriminative.

Citation

Zhang, H., Long, Y., & Zhao, C. (2018). Attribute relaxation from class level to instance level for zero-shot learning. Electronics Letters, 54(20), 1170-1172. https://doi.org/10.1049/el.2018.5027

Journal Article Type Article
Online Publication Date Oct 1, 2018
Publication Date 2018-10
Deposit Date Aug 31, 2019
Journal Electronics Letters
Print ISSN 0013-5194
Electronic ISSN 1350-911X
Publisher Institution of Engineering and Technology (IET)
Peer Reviewed Peer Reviewed
Volume 54
Issue 20
Pages 1170-1172
DOI https://doi.org/10.1049/el.2018.5027
Public URL https://durham-repository.worktribe.com/output/1294119