Learning to Rank Using Privileged Information

Viktoriia Sharmanska, Novi Quadrianto, Christoph H. Lampert; The IEEE International Conference on Computer Vision (ICCV), 2013, pp. 825-832


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.

Related Material

author = {Sharmanska, Viktoriia and Quadrianto, Novi and Lampert, Christoph H.},
title = {Learning to Rank Using Privileged Information},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {December},
year = {2013}