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We propose a method for inferring human attributes (such as gender, hair style, clothes style, expression, action) from images of people under large variation of viewpoint, pose, appearance, articulation and occlusion. Convolutional Neural Nets (CNN) have been shown to perform very well on large scale object recognition problems  . In the context of attribute classification, however, the signal is often subtle and it may cover only a small part of the image, while the image is dominated bydoi:10.1109/cvpr.2014.212 dblp:conf/cvpr/ZhangPRDB14 fatcat:pbqpvtjh4bdkfe5l6ccgffxuye