Dynamic Visual Category Learning
Abstract
Dynamic visual category learning calls for efficient adaptation as new training images become available or new categories are defined, existing training images or categories become modified or obsolete, or when categories are divided into subcategories or merged together. We develop novel methods for efficient incremental learning of SVM-based visual category classifiers to handle such dynamic tasks. Our method exploits previous classifier estimates to more efficiently learn the optimal parameters for the current set of training images and categories. We show empirically that for dynamic visual category tasks, our incremental learning methods are significantly faster than batch retraining.
Cite
Text
Yeh and Darrell. "Dynamic Visual Category Learning." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2008. doi:10.1109/CVPR.2008.4587616Markdown
[Yeh and Darrell. "Dynamic Visual Category Learning." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2008.](https://mlanthology.org/cvpr/2008/yeh2008cvpr-dynamic/) doi:10.1109/CVPR.2008.4587616BibTeX
@inproceedings{yeh2008cvpr-dynamic,
title = {{Dynamic Visual Category Learning}},
author = {Yeh, Tom and Darrell, Trevor},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2008},
doi = {10.1109/CVPR.2008.4587616},
url = {https://mlanthology.org/cvpr/2008/yeh2008cvpr-dynamic/}
}