Conference item
Knowing who to listen to: Prioritizing experts from a diverse ensemble for attribute personalization
- 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 ...
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Accepted manuscript, pdf, 211.6KB)
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- Publisher copy:
- 10.1109/ICIP.2016.7533204
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Authors
Bibliographic Details
- Publisher:
- IEEE Publisher's website
- Journal:
- 2016 IEEE International Conference on Image Processing (ICIP) Journal website
- Pages:
- 4463-4467
- Host title:
- 2016 IEEE International Conference on Image Processing (ICIP)
- Publication date:
- 2016-08-19
- Acceptance date:
- 2016-05-13
- DOI:
- ISSN:
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2381-8549
- Source identifiers:
-
811333
- ISBN:
- 9781467399616
Item Description
- Keywords:
- Pubs id:
-
pubs:811333
- UUID:
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uuid:8ae741b2-4dc2-49c5-83e2-ac8e2224c0ec
- Local pid:
- pubs:811333
- Deposit date:
- 2017-12-15
Terms of use
- 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
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