Adding Unlabeled Samples to Categories by Learned Attributes

CVPR 2013 Jonghyun ChoiMohammad RastegariAli FarhadiLarry S. Davis

We propose a method to expand the visual coverage of training sets that consist of a small number of labeled examples using learned attributes. Our optimization formulation discovers category specific attributes as well as the images that have high confidence in terms of the attributes... (read more)

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