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Learning to rank using privileged information

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conference contribution
posted on 2023-06-08, 16:45 authored by Viktoriia Sharmanska, Novi QuadriantoNovi Quadrianto, Christoph H Lampert
Many 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 2013

ISSN

1550-5499

Publisher

Institute of Electrical and Electronics Engineers

Page range

825-832

ISBN

9781479928392

Department affiliated with

  • Informatics Publications

Full text available

  • Yes

Peer reviewed?

  • Yes

Legacy Posted Date

2014-02-24

First Open Access (FOA) Date

2017-06-16

First Compliant Deposit (FCD) Date

2017-06-16

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