An efficient two-stage framework for image annotation
Pattern Recognition
Attributes for classifier feedback
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Unsupervised learning of discriminative relative visual attributes
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part III
Learning attribute relation in attribute-based zero-shot classification
IScIDE'12 Proceedings of the third Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
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We present an active learning approach to choose image annotation requests among both object category labels and the objects' attribute labels. The goal is to solicit those labels that will best use human effort when training a multi-class object recognition model. In contrast to previous work in active visual category learning, our approach directly exploits the dependencies between human-nameable visual attributes and the objects they describe, shifting its requests in either label space accordingly. We adopt a discriminative latent model that captures object-attribute and attribute-attribute relationships, and then define a suitable entropy reduction selection criterion to predict the influence a new label might have throughout those connections. On three challenging datasets, we demonstrate that the method can more successfully accelerate object learning relative to both passive learning and traditional active learning approaches.