Attribute-based detection of unfamiliar classes with humans in the loop
Research output: Contribution to journal › Conference article › Research › peer-review
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.
Original language | English |
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Article number | 6618950 |
Journal | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
Pages (from-to) | 779-786 |
Number of pages | 8 |
ISSN | 1063-6919 |
DOIs | |
Publication status | Published - 2013 |
Externally published | Yes |
Event | 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2013 - Portland, OR, United States Duration: 23 Jun 2013 → 28 Jun 2013 |
Conference
Conference | 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2013 |
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Country | United States |
City | Portland, OR |
Period | 23/06/2013 → 28/06/2013 |
Sponsor | IEEE Computer Society |
- attribute-based classification, fine-grained visual categories, human in the loop, unfamiliar class detection, visual recognition
Research areas
ID: 302047231