Learning Attributes Equals Multi-Source Domain Generalization
Abstract
Attributes possess appealing properties and benefit many computer vision problems, such as object recognition, learning with humans in the loop, and image retrieval. Whereas the existing work mainly pursues utilizing attributes for various computer vision problems, we contend that the most basic problem - how to accurately and robustly detect attributes from images - has been left under explored. Especially, the existing work rarely explicitly tackles the need that attribute detectors should generalize well across different categories, including those previously unseen. Noting that this is analogous to the objective of multi-source domain generalization, if we treat each category as a domain, we provide a novel perspective to attribute detection and propose to gear the techniques in multi-source domain generalization for the purpose of learning cross-category generalizable attribute detectors. We validate our understanding and approach with extensive experiments on four challenging datasets and three different problems.
Publication Date
12-9-2016
Publication Title
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Volume
2016-December
Number of Pages
87-97
Document Type
Article; Proceedings Paper
Personal Identifier
scopus
DOI Link
https://doi.org/10.1109/CVPR.2016.17
Copyright Status
Unknown
Socpus ID
84986281512 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/84986281512
STARS Citation
Gan, Chuang; Yang, Tianbao; and Gong, Boqing, "Learning Attributes Equals Multi-Source Domain Generalization" (2016). Scopus Export 2015-2019. 4547.
https://stars.library.ucf.edu/scopus2015/4547