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Representing shape with a spatial pyramid kernel
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Large Scale Online Learning of Image Similarity through Ranking
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Fast Similarity Search for Learned Metrics
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Discovering latent domains for multisource domain adaptation
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No bias left behind: covariate shift adaptation for discriminative 3d pose estimation
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FIDOS: A generalized Fisher based feature extraction method for domain shift
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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 nonimage 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.