We describe a learning-based method for low-level vision problems--estimating scenes from images. We generate a synthetic world of scenes and their corresponding rendered images, m...
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 recogniti...
Kate Saenko, Brian Kulis, Mario Fritz, Trevor Darr...
Automatic visual categorization is critically dependent on labeled examples for supervised learning. As an alternative to traditional expert labeling, social-tagged multimedia is ...
This paper illustrates how canonical correlation analysis can be used for designing efficient visual operators by learning. The approach is highly task oriented and what constitute...
Three-dimensional electron-microscopic image stacks with almost isotropic resolution allow, for the first time, to determine the complete connection matrix of parts of the brain. I...