Transferring Visual Category Models to New Domains

We propose a method to perform adaptive transfer of visual category knowledge from labeled datasets acquired in one image domain to other environments. We learn a representation which minimizes the effect of shifting between source and target domains using a novel metric learning approach. The key idea of our approach to domain adaptation is to learn a metric that compensates for the transformation of the object representation that occurred due to the domain shift. In addition to being one of the first studies of domain adaptation for object recognition, this work develops a general adaptation technique that could be applied to non-image data. Another contribution is a new image database for studying the effects of visual domain shift on object recognition. We demonstrate the ability of our adaptation method to improve performance of classifiers on new domains that have very little labeled data.