Learning to rank using privileged information

Sharmanska, Viktoriia, Quadrianto, Novi and Lampert, Christoph H (2014) Learning to rank using privileged information. Published in: Proceedings of the 2013 IEEE International Conference on Computer Vision (ICCV); Sydney, Australia; 1 - 8 December 2013. 825-832. Institute of Electrical and Electronics Engineers ISSN 1550-5499 ISBN 9781479928392

[img]
Preview
PDF - Accepted Version
Download (1MB) | Preview

Abstract

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.

Item Type: Conference Proceedings
Schools and Departments: School of Engineering and Informatics > Informatics
Subjects: Q Science > Q Science (General)
Related URLs:
Depositing User: Novi Quadrianto
Date Deposited: 24 Feb 2014 14:44
Last Modified: 16 Jun 2017 10:57
URI: http://sro.sussex.ac.uk/id/eprint/47616

View download statistics for this item

📧 Request an update