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