TY - GEN
T1 - Comparative object similarity for improved recognition with few or no examples
AU - Wang, Gang
AU - Forsyth, David
AU - Hoiem, Derek
PY - 2010
Y1 - 2010
N2 - Learning models for recognizing objects with few or no training examples is important, due to the intrinsic long-tailed distribution of objects in the real world. In this paper, we propose an approach to use comparative object similarity. The key insight is that: given a set of object categories which are similar and a set of categories which are dissimilar, a good object model should respond more strongly to examples from similar categories than to examples from dissimilar categories. We develop a regularized kernel machine algorithm to use this category dependent similarity regularization. Our experiments on hundreds of categories show that our method can make significant improvement, especially for categories with no examples.
AB - Learning models for recognizing objects with few or no training examples is important, due to the intrinsic long-tailed distribution of objects in the real world. In this paper, we propose an approach to use comparative object similarity. The key insight is that: given a set of object categories which are similar and a set of categories which are dissimilar, a good object model should respond more strongly to examples from similar categories than to examples from dissimilar categories. We develop a regularized kernel machine algorithm to use this category dependent similarity regularization. Our experiments on hundreds of categories show that our method can make significant improvement, especially for categories with no examples.
UR - http://www.scopus.com/inward/record.url?scp=77955997493&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77955997493&partnerID=8YFLogxK
U2 - 10.1109/CVPR.2010.5539955
DO - 10.1109/CVPR.2010.5539955
M3 - Conference contribution
AN - SCOPUS:77955997493
SN - 9781424469840
T3 - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
SP - 3525
EP - 3532
BT - 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2010
T2 - 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2010
Y2 - 13 June 2010 through 18 June 2010
ER -