Jump to : Download | Abstract | Contact | BibTex reference | EndNote reference |


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.

Download [help]

Download paper: Adobe portable document (pdf)

Copyright notice:This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.


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

BibTex Reference

   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}

EndNote Reference [help]

Get EndNote Reference (.ref)


For problems or questions regarding this web site contact The Web Master.

This document was translated automatically from BibTEX by bib2html (Copyright 2003 © Eric Marchand, INRIA, Vista Project).