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Handling Uncertain Tags in Visual Recognition
AbstractGathering 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
[pdf][bibtex]@InProceedings{Vahdat_2013_ICCV,
author = {Vahdat, Arash and Mori, Greg},
title = {Handling Uncertain Tags in Visual Recognition},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},
month = {December},
year = {2013}
}
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