11. A method for associating part proposal with semantic parts, the method comprising: sampling a set of points in a single image of a subset of images, each image is annotated with one or more localized attribute labels; propagating the set of points to every other image of the subset of images based on learned alignments; generating a plurality of part proposals by sampling regions around the set of points yielding a set of regions in each image corresponding to exactly one other region per image in the subset of images; assuming that a first part proposal is actually a first semantic part; training a classifier at the first part proposal for the attributes localized at the first semantic part; and computing an information gain between the classifier and ground truth attributes of other semantic parts of the image.
11. A method for associating part proposal with semantic parts, the method comprising: sampling a set of points in a single image of a subset of images, each image is annotated with one or more localized attribute labels; propagating the set of points to every other image of the subset of images based on learned alignments; generating a plurality of part proposals by sampling regions around the set of points yielding a set of regions in each image corresponding to exactly one other region per image in the subset of images; assuming that a first part proposal is actually a first semantic part; training a classifier at the first part proposal for the attributes localized at the first semantic part; and computing an information gain between the classifier and ground truth attributes of other semantic parts of the image. 16. The method of claim 11 , determining the information gain at other semantic parts of the image does not match an expected pattern.