Pooling Attention-based Encoder–Decoder Network for semantic segmentation. (July 2021)

• Record Type: Journal Article
• Authors: Xu, Haixia; Huang, Yunjia; Hancock, Edwin R.; Wang, Shuailong; Xuan, Qijun; Zhou, Wei
• Is Part Of: Computers & electrical engineering. Volume 93(2021)
• Publication Date: 2021-07
• Subjects: Semantic segmentation -- Encoder–Decoder -- Pooling attention module -- Channel -- Position; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2021.107260
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: Aiming to the challenge of poor pixel-consistency in inter-category and pixel-similarity in inter-category, in this paper, we propose an Encoder–Decoder network for image semantic segmentation using pooling SE-ResNet attention module, called PAEDN. It is an effective of attention mechanism to get aggregated information. According to the principle of SE-ResNet, a collection of Average, Maximum and Stochastic global pooling, which concentrate on contoured, detailed, and generalized information in a certain semantic segmentation, form attention modules. Channel Pooling Attention Module (CPAM) and Position Pooling Attention Module (PPAM) are designed and integrated into the Encoder to extract discriminative features from input images, and the Decoder is developed through SE-ResNet attention module to fuse the feature map in high-resolution with that in low-resolution. Experimental evaluations performed on the data sets PASCAL and Cityscapes, show the proposed Encoder–Decoder with pooling attention module produces good pixel-consistency semantic label, achieves 15.1% improvement to FCN. Graphical abstract: Highlights: The encoder and decoder work together to enhance the consistency of pixels. The work uses maximum, average and stochastic pooling to gain contoured, detailed and generalized information. Two attention modules use pooling operations to integrate discriminative feature information.