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A Multisource Domain Generalization Approach to Visual Attribute Detection
Book chapter   Open access

A Multisource Domain Generalization Approach to Visual Attribute Detection

Chuang Gan, Tianbao Yang and Boqing Gong
Domain Adaptation in Computer Vision Applications, pp.277-289
Advances in Computer Vision and Pattern Recognition, Springer International Publishing
09/13/2017
DOI: 10.1007/978-3-319-58347-1_15
url
https://stars.library.ucf.edu/scopus2015/6473View
Open Access

Abstract

Attributes possess appealing properties and benefit many computer vision problems, such as object recognition, learningImage categorizationobject recognition 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 underexplored. 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 multisource domain generalization, ifDomain generalization we treat each category as a domain, we provide a novel perspective to attribute detection and propose to gear the techniques in multisource 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 two different problems.

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