- Abstract:
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Learning attribute models for applications like Zero-Shot Learning (ZSL) and image search is challenging because they require attribute classifiers to generalize to test data that may be very different from the training data. A typical scenario is when the notion of an attribute may differ from one user to another, e.g. one user may find a shoe formal whereas another user may not. In this case, the distribution of labels at test time is different from that at training time. We argue that due ...
Expand abstract - Publication status:
- Published
- Peer review status:
- Peer reviewed
- Version:
- Accepted Manuscript
- Files:
-
-
(pdf, 211.6KB)
-
- Publisher copy:
- 10.1109/ICIP.2016.7533204
-
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- Publisher:
- IEEE Publisher's website
- Pages:
- 4463-4467
- Publication date:
- 2016-08-19
- Acceptance date:
- 2016-05-13
- DOI:
- ISSN:
-
2381-8549
- Pubs id:
-
pubs:811333
- URN:
-
uri:8ae741b2-4dc2-49c5-83e2-ac8e2224c0ec
- UUID:
-
uuid:8ae741b2-4dc2-49c5-83e2-ac8e2224c0ec
- Local pid:
- pubs:811333
- ISBN:
- 978-1-4673-9961-6
- Copyright holder:
- IEEE
- Copyright date:
- 2016
- Notes:
-
Copyright © 2016 IEEE. This is the accepted manuscript version of the paper. The final version is available online from IEEE at: https://doi.org/10.1109/ICIP.2016.7533204
Conference item
Knowing who to listen to: Prioritizing experts from a diverse ensemble for attribute personalization
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