Adapting Visual Category Models to New Domains
Abstract
Domain adaptation is an important emerging topic in computer vision. In this paper, we present one of the first studies of domain shift in the context of object recognition. We introduce a method that adapts object models acquired in a particular visual domain to new imaging conditions by learning a transformation that minimizes the effect of domain-induced changes in the feature distribution. The transformation is learned in a supervised manner and can be applied to categories for which there are no labeled examples in the new domain. While we focus our evaluation on object recognition tasks, the transform-based adaptation technique we develop is general and could be applied to non-image data. Another contribution is a new multi-domain object database, freely available for download. We experimentally demonstrate the ability of our method to improve recognition on categories with few or no target domain labels and moderate to large changes in the imaging conditions.
Cite
Text
Saenko et al. "Adapting Visual Category Models to New Domains." European Conference on Computer Vision, 2010. doi:10.1007/978-3-642-15561-1_16Markdown
[Saenko et al. "Adapting Visual Category Models to New Domains." European Conference on Computer Vision, 2010.](https://mlanthology.org/eccv/2010/saenko2010eccv-adapting/) doi:10.1007/978-3-642-15561-1_16BibTeX
@inproceedings{saenko2010eccv-adapting,
title = {{Adapting Visual Category Models to New Domains}},
author = {Saenko, Kate and Kulis, Brian and Fritz, Mario and Darrell, Trevor},
booktitle = {European Conference on Computer Vision},
year = {2010},
pages = {213-226},
doi = {10.1007/978-3-642-15561-1_16},
url = {https://mlanthology.org/eccv/2010/saenko2010eccv-adapting/}
}