Handling Uncertain Tags in Visual Recognition
Abstract
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.
Cite
Text
Vahdat and Mori. "Handling Uncertain Tags in Visual Recognition." International Conference on Computer Vision, 2013. doi:10.1109/ICCV.2013.462Markdown
[Vahdat and Mori. "Handling Uncertain Tags in Visual Recognition." International Conference on Computer Vision, 2013.](https://mlanthology.org/iccv/2013/vahdat2013iccv-handling/) doi:10.1109/ICCV.2013.462BibTeX
@inproceedings{vahdat2013iccv-handling,
title = {{Handling Uncertain Tags in Visual Recognition}},
author = {Vahdat, Arash and Mori, Greg},
booktitle = {International Conference on Computer Vision},
year = {2013},
doi = {10.1109/ICCV.2013.462},
url = {https://mlanthology.org/iccv/2013/vahdat2013iccv-handling/}
}