Sharmanska_Learning_to_Rank_2013_ICCV_paper.pdf (1.13 MB)
Learning to rank using privileged information
conference contribution
posted on 2023-06-08, 16:45 authored by Viktoriia Sharmanska, Novi QuadriantoNovi Quadrianto, Christoph H LampertMany computer vision problems have an asymmetric distribution of information between training and test time. In this work, we study the case where we are given additional information about the training data, which however will not be available at test time. This situation is called learning using privileged information (LUPI). We introduce two maximum-margin techniques that are able to make use of this additional source of information, and we show that the framework is applicable to several scenarios that have been studied in computer vision before. Experiments with attributes, bounding boxes, image tags and rationales as additional information in object classification show promising results.
History
Publication status
- Published
File Version
- Accepted version
Journal
Proceedings of the 2013 IEEE International Conference on Computer Vision (ICCV); Sydney, Australia; 1 - 8 December 2013ISSN
1550-5499Publisher
Institute of Electrical and Electronics EngineersExternal DOI
Page range
825-832ISBN
9781479928392Department affiliated with
- Informatics Publications
Full text available
- Yes
Peer reviewed?
- Yes
Legacy Posted Date
2014-02-24First Open Access (FOA) Date
2017-06-16First Compliant Deposit (FCD) Date
2017-06-16Usage metrics
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