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2019 | OriginalPaper | Chapter

Graph-Boosted Attentive Network for Semantic Body Parsing

Authors : Tinghuai Wang, Huiling Wang

Published in: Artificial Neural Networks and Machine Learning – ICANN 2019: Image Processing

Publisher: Springer International Publishing

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Abstract

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.

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Title: Graph-Boosted Attentive Network for Semantic Body Parsing
Authors: Tinghuai Wang
Huiling Wang
Publisher: Springer International Publishing
Book: Artificial Neural Networks and Machine Learning – ICANN 2019: Image Processing
Print ISBN: 978-3-030-30507-9

Electronic ISBN: 978-3-030-30508-6

Copyright Year: 2019
DOI: https://doi.org/10.1007/978-3-030-30508-6_22

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