Attribute-based detection of unfamiliar classes with humans in the loop
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Attribute-based detection of unfamiliar classes with humans in the loop. / Wah, Catherine; Belongie, Serge.
In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2013, p. 779-786.Research output: Contribution to journal › Conference article › Research › peer-review
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TY - GEN
T1 - Attribute-based detection of unfamiliar classes with humans in the loop
AU - Wah, Catherine
AU - Belongie, Serge
PY - 2013
Y1 - 2013
N2 - Recent work in computer vision has addressed zero-shot learning or unseen class detection, which involves categorizing objects without observing any training examples. However, these problems assume that attributes or defining characteristics of these unobserved classes are known, leveraging this information at test time to detect an unseen class. We address the more realistic problem of detecting categories that do not appear in the dataset in any form. We denote such a category as an unfamiliar class, it is neither observed at train time, nor do we possess any knowledge regarding its relationships to attributes. This problem is one that has received limited attention within the computer vision community. In this work, we propose a novel approach to the unfamiliar class detection task that builds on attribute-based classification methods, and we empirically demonstrate how classification accuracy is impacted by attribute noise and dataset 'difficulty,' as quantified by the separation of classes in the attribute space. We also present a method for incorporating human users to overcome deficiencies in attribute detection. We demonstrate results superior to existing methods on the challenging CUB-200-2011 dataset.
AB - Recent work in computer vision has addressed zero-shot learning or unseen class detection, which involves categorizing objects without observing any training examples. However, these problems assume that attributes or defining characteristics of these unobserved classes are known, leveraging this information at test time to detect an unseen class. We address the more realistic problem of detecting categories that do not appear in the dataset in any form. We denote such a category as an unfamiliar class, it is neither observed at train time, nor do we possess any knowledge regarding its relationships to attributes. This problem is one that has received limited attention within the computer vision community. In this work, we propose a novel approach to the unfamiliar class detection task that builds on attribute-based classification methods, and we empirically demonstrate how classification accuracy is impacted by attribute noise and dataset 'difficulty,' as quantified by the separation of classes in the attribute space. We also present a method for incorporating human users to overcome deficiencies in attribute detection. We demonstrate results superior to existing methods on the challenging CUB-200-2011 dataset.
KW - attribute-based classification
KW - fine-grained visual categories
KW - human in the loop
KW - unfamiliar class detection
KW - visual recognition
UR - http://www.scopus.com/inward/record.url?scp=84887334314&partnerID=8YFLogxK
U2 - 10.1109/CVPR.2013.106
DO - 10.1109/CVPR.2013.106
M3 - Conference article
AN - SCOPUS:84887334314
SP - 779
EP - 786
JO - I E E E Conference on Computer Vision and Pattern Recognition. Proceedings
JF - I E E E Conference on Computer Vision and Pattern Recognition. Proceedings
SN - 1063-6919
M1 - 6618950
T2 - 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2013
Y2 - 23 June 2013 through 28 June 2013
ER -
ID: 302047231