Felix Yu, Liangliang Cao, Rogerio Feris, John Smith, Shih-Fu Chang. Designing category-level attributes for discriminative visual recognition. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Portland, OR, June 2013.
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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-of-the-art performance on the zero-shot learning task on AwA
FelixX. Yu 
John_R. Smith
Shih-Fu Chang
@InProceedings{CVPR13:attribute,
Author = {Yu, Felix and Cao, Liangliang and Feris, Rogerio and Smith, John and Chang, Shih-Fu},
Title = {Designing category-level attributes for discriminative visual recognition},
BookTitle = {IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR)},
Address = {Portland, OR},
Month = {June},
Year = {2013}
}
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