Handling Uncertain Tags in Visual Recognition

Arash Vahdat, Greg Mori; The IEEE International Conference on Computer Vision (ICCV), 2013, pp. 737-744


Gathering accurate training data for recognizing a set of attributes or tags on images or videos is a challenge. Obtaining labels via manual effort or from weakly-supervised data typically results in noisy training labels. We develop the FlipSVM, a novel algorithm for handling these noisy, structured labels. The FlipSVM models label noise by "flipping" labels on training examples. We show empirically that the FlipSVM is effective on images-and-attributes and video tagging datasets.

Related Material

author = {Vahdat, Arash and Mori, Greg},
title = {Handling Uncertain Tags in Visual Recognition},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {December},
year = {2013}