In Defence of Negative Mining for Annotating Weakly Labelled Data
Abstract
We propose a novel approach to annotating weakly labelled data. In contrast to many existing approaches that perform annotation by seeking clusters of self-similar exemplars (minimising intra-class variance), we perform image annotation by selecting exemplars that have never occurred before in the much larger, and strongly annotated, negative training set (maximising inter-class variance). Compared to existing methods, our approach is fast, robust, and obtains state of the art results on two challenging data-sets – voc 2007 (all poses), and the msr 2 action data-set, where we obtain a 10% increase. Moreover, this use of negative mining complements existing methods, that seek to minimize the intra-class variance, and can be readily integrated with many of them.
Cite
Text
Siva et al. "In Defence of Negative Mining for Annotating Weakly Labelled Data." European Conference on Computer Vision, 2012. doi:10.1007/978-3-642-33712-3_43Markdown
[Siva et al. "In Defence of Negative Mining for Annotating Weakly Labelled Data." European Conference on Computer Vision, 2012.](https://mlanthology.org/eccv/2012/siva2012eccv-defence/) doi:10.1007/978-3-642-33712-3_43BibTeX
@inproceedings{siva2012eccv-defence,
title = {{In Defence of Negative Mining for Annotating Weakly Labelled Data}},
author = {Siva, Parthipan and Russell, Chris and Xiang, Tao},
booktitle = {European Conference on Computer Vision},
year = {2012},
pages = {594-608},
doi = {10.1007/978-3-642-33712-3_43},
url = {https://mlanthology.org/eccv/2012/siva2012eccv-defence/}
}