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Spatial Channel Attention for Deep Convolutional Neural Networks

Author

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  • Tonglai Liu

    (College of Information Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
    Smart Agriculture Engineering Technology Research Center of Guangdong Higher Education Institutes, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
    Guangzhou Key Laboratory of Agricultural Products Quality & Safety Traceability Information Technology, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
    Academy of Smart Agricultural Engineering Innovations, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China)

  • Ronghai Luo

    (School of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, China
    These authors contributed equally to this work.)

  • Longqin Xu

    (College of Information Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
    Smart Agriculture Engineering Technology Research Center of Guangdong Higher Education Institutes, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
    Guangzhou Key Laboratory of Agricultural Products Quality & Safety Traceability Information Technology, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
    Academy of Smart Agricultural Engineering Innovations, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China)

  • Dachun Feng

    (College of Information Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
    Smart Agriculture Engineering Technology Research Center of Guangdong Higher Education Institutes, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
    Guangzhou Key Laboratory of Agricultural Products Quality & Safety Traceability Information Technology, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
    Academy of Smart Agricultural Engineering Innovations, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China)

  • Liang Cao

    (College of Information Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
    Smart Agriculture Engineering Technology Research Center of Guangdong Higher Education Institutes, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
    Guangzhou Key Laboratory of Agricultural Products Quality & Safety Traceability Information Technology, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
    Academy of Smart Agricultural Engineering Innovations, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China)

  • Shuangyin Liu

    (College of Information Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
    Smart Agriculture Engineering Technology Research Center of Guangdong Higher Education Institutes, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
    Guangzhou Key Laboratory of Agricultural Products Quality & Safety Traceability Information Technology, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
    Academy of Smart Agricultural Engineering Innovations, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China)

  • Jianjun Guo

    (College of Information Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
    Smart Agriculture Engineering Technology Research Center of Guangdong Higher Education Institutes, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
    Guangzhou Key Laboratory of Agricultural Products Quality & Safety Traceability Information Technology, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
    Academy of Smart Agricultural Engineering Innovations, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China)

Abstract

Recently, the attention mechanism combining spatial and channel information has been widely used in various deep convolutional neural networks (CNNs), proving its great potential in improving model performance. However, this usually uses 2D global pooling operations to compress spatial information or scaling methods to reduce the computational overhead in channel attention. These methods will result in severe information loss. Therefore, we propose a Spatial channel attention mechanism that captures cross-dimensional interaction, which does not involve dimensionality reduction and brings significant performance improvement with negligible computational overhead. The proposed attention mechanism can be seamlessly integrated into any convolutional neural network since it is a lightweight general module. Our method achieves a performance improvement of 2.08% on ResNet and 1.02% on MobileNetV2 in top-one error rate on the ImageNet dataset.

Suggested Citation

  • Tonglai Liu & Ronghai Luo & Longqin Xu & Dachun Feng & Liang Cao & Shuangyin Liu & Jianjun Guo, 2022. "Spatial Channel Attention for Deep Convolutional Neural Networks," Mathematics, MDPI, vol. 10(10), pages 1-10, May.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:10:p:1750-:d:820162
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    Citations

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    Cited by:

    1. Seng Chun Hoo & Haidi Ibrahim & Shahrel Azmin Suandi & Theam Foo Ng, 2023. "LCAM: Low-Complexity Attention Module for Lightweight Face Recognition Networks," Mathematics, MDPI, vol. 11(7), pages 1-27, April.
    2. Xiaoyong Zhang & Rui Xu & Kaixuan Lu & Zhihang Hao & Zhengchao Chen & Mingyong Cai, 2022. "Resource-Based Port Material Yard Detection with SPPA-Net," Sustainability, MDPI, vol. 14(24), pages 1-12, December.

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