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Class-level Aware Network for Human Parsing

Jiayi YinWeibin LiuWeiwei XingYuan Xiao

Institute of Information Science,Beijing Jiaotong UniversitySchool of Software Engineering,Beijing Jiaotong UniversityBidding and Purchasing Management Center,Renmin University of China

摘要: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.
会议名称:

2021 2nd International Conference on Computing, Networks and Internet of Things

会议时间:

2021-05-20

会议地点:

中国北京

  • 专辑:

    信息科技

  • 专题:

    计算机软件及计算机应用

  • DOI:

    10.26914/c.cnkihy.2021.013516

  • 分类号:

    TP391.41

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