  Human body parsing remains a challenging problem in natural scenes due to
multi-instance and inter-part semantic confusions as well as occlusions. This
paper proposes a novel approach to decomposing multiple human bodies into
semantic part regions in unconstrained environments. Specifically we propose a
convolutional neural network (CNN) architecture which comprises of novel
semantic and contour attention mechanisms across feature hierarchy to resolve
the semantic ambiguities and boundary localization issues related to semantic
body parsing. We further propose to encode estimated pose as higher-level
contextual information which is combined with local semantic cues in a novel
graphical model in a principled manner. In this proposed model, the lower-level
semantic cues can be recursively updated by propagating higher-level contextual
information from estimated pose and vice versa across the graph, so as to
alleviate erroneous pose information and pixel level predictions. We further
propose an optimization technique to efficiently derive the solutions. Our
proposed method achieves the state-of-art results on the challenging Pascal
Person-Part dataset.
