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Constrained Semi-Supervised Learning using Attributes and Comparative Attributes

Constrained Semi-Supervised Learning using Attributes and Comparative Attributes

This video was recorded at 12th European Conference on Computer Vision (ECCV), Firenze 2012. We consider the problem of semi-supervised bootstrap learning for scene categorization. Existing semi-supervised approaches are typically unreliable and face semantic drift because the learning task is under-constrained. This is primarily because they ignore the strong interactions that often exist between scene categories, such as the common attributes shared across categories as well as the attributes which make one scene different from another. The goal of this paper is to exploit these relationships and constrain the semi-supervised learning problem. For example, the knowledge that an image is an auditorium can improve labeling of amphitheaters by enforcing constraint that an amphitheater image... Show More
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