Designing Category-Level Attributes for Discriminative Visual Recognition

Felix X. Yu, Liangliang Cao, Rogerio S. Feris, John R. Smith, Shih-Fu Chang; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013, pp. 771-778

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.

Related Material


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[bibtex]
@InProceedings{Yu_2013_CVPR,
author = {Yu, Felix X. and Cao, Liangliang and Feris, Rogerio S. and Smith, John R. and Chang, Shih-Fu},
title = {Designing Category-Level Attributes for Discriminative Visual Recognition},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2013}
}