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We propose a novel method for discovering shape regions that strongly correlate with user-prescribed tags. For example, given a collection of chairs tagged as either "has armrest" or "lacks armrest", our system correctly highlights the armrest regions as the main distinctive parts between the two chair types. To obtain point-wise predictions from shape-wise tags we develop a novel neural network architecture that is trained with tag classification loss, but is designed to rely on segmentationdoi:10.1109/cvpr.2018.00309 dblp:conf/cvpr/MuralikrishnanK18 fatcat:umn5zvwxerevlocfrijmu67w3u