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Erschienen in:
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2020 | OriginalPaper | Buchkapitel

Blended Grammar Network for Human Parsing

verfasst von : Xiaomei Zhang, Yingying Chen, Bingke Zhu, Jinqiao Wang, Ming Tang

Erschienen in: Computer Vision – ECCV 2020

Verlag: Springer International Publishing

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Abstract

Although human parsing has made great progress, it still faces a challenge, i.e., how to extract the whole foreground from similar or cluttered scenes effectively. In this paper, we propose a Blended Grammar Network (BGNet), to deal with the challenge. BGNet exploits the inherent hierarchical structure of a human body and the relationship of different human parts by means of grammar rules in both cascaded and paralleled manner. In this way, conspicuous parts, which are easily distinguished from the background, can amend the segmentation of inconspicuous ones, improving the foreground extraction. We also design a Part-aware Convolutional Recurrent Neural Network (PCRNN) to pass messages which are generated by grammar rules. To train PCRNNs effectively, we present a blended grammar loss to supervise the training of PCRNNs. We conduct extensive experiments to evaluate BGNet on PASCAL-Person-Part, LIP, and PPSS datasets. BGNet obtains state-of-the-art performance on these human parsing datasets.

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Vorheriges Kapitel Unsupervised Learning of Optical Flow with Deep Feature Similarity
Nächstes Kapitel PNet: Patch-Match and Plane-Regularization for Unsupervised Indoor Depth Estimation
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Metadaten
Titel
Blended Grammar Network for Human Parsing
verfasst von
Xiaomei Zhang
Yingying Chen
Bingke Zhu
Jinqiao Wang
Ming Tang
Copyright-Jahr
2020
Buch
Computer Vision – ECCV 2020

Print ISBN: 978-3-030-58585-3

Electronic ISBN: 978-3-030-58586-0

Copyright-Jahr: 2020

DOI
https://doi.org/10.1007/978-3-030-58586-0_12

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