Cited By
View all- Michieli UZanuttigh P(2022)Edge-Aware Graph Matching Network for Part-Based Semantic SegmentationInternational Journal of Computer Vision10.1007/s11263-022-01671-z130:11(2797-2821)Online publication date: 1-Nov-2022
Class-level Aware Network for Human Parsing
Authors: 
Jiayi Yin, Weibin Liu, Weiwei Xing, Yuan XiaoAuthors Info & Claims
Jiayi Yin, Weibin Liu, Weiwei Xing, Yuan XiaoAuthors Info & ClaimsArticle No.: 37, Pages 1 - 6
Abstract
Having shown great performance in human parsing, convolutional neural networks(CNNs) come with much computation budget. In this paper, a novel class-level aware network(CANet), which employs an asymmetric encoder-decoder architecture, is presented to achieve reliable human parsing results in a memory friendly way. To achieve the trade-off between speed and accuracy in human parsing, we design group-split-bottleneck(GS-bt) block, where group convolution and channel split are utilized in the residual block. In decoder network, the attention pyramid pooling module(APPM) is proposed to recovering the details of human parsing. Moreover, a multi-class classification branch is developed to extract class-level information and revise human parsing results. Compared to current models, our model has less parameters and experiments demonstrate that the proposed CANet can reach state-of-the-art results on PASCAL-Person-Part dataset.
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Index Terms
- Class-level Aware Network for Human Parsing
Index terms have been assigned to the content through auto-classification.
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May 2021
270 pages
ISBN:9781450389693
DOI:10.1145/3468691
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Published: 07 August 2021
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CNIOT2021
CNIOT2021: 2021 2nd International Conference on Computing, Networks and Internet of Things
May 20 - 22, 2021
Beijing, China
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Cited By
View all- Michieli UZanuttigh P(2022)Edge-Aware Graph Matching Network for Part-Based Semantic SegmentationInternational Journal of Computer Vision10.1007/s11263-022-01671-z130:11(2797-2821)Online publication date: 1-Nov-2022
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