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Designing Category-Level Attributes for Discriminative Visual Recognition ∗
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BibTeX
@MISC{Yu_designingcategory-level,
author = {Felix X. Yu and Liangliang Cao and Rogerio S. Feris and John R. Smith and Shih-fu Chang},
title = {Designing Category-Level Attributes for Discriminative Visual Recognition ∗},
year = {}
}
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Abstract
Attribute-based representation has shown great promises for visual recognition due to its intuitive interpretation and cross-category generalization property. However, human efforts are usually involved in the attribute designing process, making the representation costly to obtain. In this paper, we propose a novel formulation to automatically design discriminative “category-level attributes”, which can be efficiently encoded by a compact category-attribute matrix. The formulation allows us to achieve intuitive and critical design criteria (category-separability, learnability) in a principled way. The designed attributes can be used for tasks of cross-category knowledge transfer, achieving superior performance over well-known attribute dataset Animals with Attributes (AwA) and a large-scale ILSVRC2010 dataset (1.2M images). This approach also leads to state-ofthe-art performance on the zero-shot learning task on AwA. 1.
Keyphrases
category-level attribute discriminative visual recognition large-scale ilsvrc2010 dataset critical design criterion compact category-attribute matrix state-ofthe-art performance human effort superior performance principled way cross-category generalization property visual recognition cross-category knowledge transfer attribute-based representation zero-shot learning task novel formulation great promise intuitive interpretation discriminative category-level attribute well-known attribute dataset animal
