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    • Double-pooling residual classification network based on feature reordering attention mechanism

    • In the field of image classification, FDPRNet significantly improves classification accuracy and model generalization ability through feature reordering attention mechanism and dual pooling residual structure.
    • Vol. 30, Issue 1, Pages: 110-129(2025)   

      Received:04 February 2024

      Revised:2024-05-13

      Published:16 January 2025

    • DOI: 10.11834/jig.240061     

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  • Yuan Heng, Liu Jie, Jiang Wentao, Liu Wanjun. 2025. Double-pooling residual classification network based on feature reordering attention mechanism. Journal of Image and Graphics, 30(01):0110-0129 DOI: 10.11834/jig.240061.
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相关作者

Bei Lu 南昌航空大学信息工程学院
Shan Gai 南昌航空大学信息工程学院
Jialin Yang 太原理工大学电气与动力工程学院
Xuejun Guo 太原理工大学大数据学院
Zehua Chen 太原理工大学大数据学院
Wang Weijia 福州大学计算机与大数据学院
Chen Fei 福州大学计算机与大数据学院
Liu Wanling 福州大学计算机与大数据学院;天津大学智能与计算学部

相关机构

School of Information Engineering, Nanchang Hangkong University
Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition
College of Electric and Power Engineering, Taiyuan University of Technology
College of Data Science, Taiyuan University of Technology
College of Computer and Data Science, Fuzhou University
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