NDLI: Human Parsing with Contextualized Convolutional Neural Network

Content Provider	IEEE Xplore Digital Library
Author	Liang, Xiaodan Xu, Chunyan Shen, Xiaohui Yang, Jianchao Liu, Si Tang, Jinhui Lin, Liang Yan, Shuicheng
Copyright Year	2015
Abstract	In this work, we address the human parsing task with a novel Contextualized Convolutional Neural Network (Co-CNN) architecture, which well integrates the cross-layer context, global image-level context, within-super-pixel context and cross-super-pixel neighborhood context into a unified network. Given an input human image, Co-CNN produces the pixel-wise categorization in an end-to-end way. First, the cross-layer context is captured by our basic local-to-global-to-local structure, which hierarchically combines the global semantic structure and the local fine details within the cross-layers. Second, the global image-level label prediction is used as an auxiliary objective in the intermediate layer of the Co-CNN, and its outputs are further used for guiding the feature learning in subsequent convolutional layers to leverage the global image-level context. Finally, to further utilize the local super-pixel contexts, the within-super-pixel smoothing and cross-super-pixel neighbourhood voting are formulated as natural sub-components of the Co-CNN to achieve the local label consistency in both training and testing process. Comprehensive evaluations on two public datasets well demonstrate the significant superiority of our Co-CNN architecture over other state-of-the-arts for human parsing. In particular, the F-1 score on the large dataset [15] reaches 76.95% by Co-CNN, significantly higher than 62.81% and 64.38% by the state-of-the-art algorithms, M-CNN [21] and ATR [15], respectively.
Starting Page	1386
Ending Page	1394
File Size	1429668
Page Count	9
File Format	PDF
ISSN	23807504
e-ISBN	9781467383912
DOI	10.1109/ICCV.2015.163
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-12-07
Publisher Place	Chile
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Context Semantics Spatial resolution Smoothing methods Labeling Neural networks Training
Content Type	Text
Resource Type	Article

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