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ECCV
2010
Springer

Adapting Visual Category Models to New Domains

3 years 10 months ago
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.
Kate Saenko, Brian Kulis, Mario Fritz, Trevor Darr
Added 09 Nov 2010
Updated 09 Nov 2010
Type Conference
Year 2010
Where ECCV
Authors Kate Saenko, Brian Kulis, Mario Fritz, Trevor Darrell
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