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Feature Channel Adaptive Enhancement for Fine-Grained Visual Classification

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Pattern Recognition (ACPR 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14408))

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Abstract

Fine-grained classification poses greater challenges compared to basic-level image classification due to the visually similar sub-species. To distinguish between confusing species, we introduce a novel framework based on feature channel adaptive enhancement and attention erasure. On one hand, a lightweight module employing both channel attention and spatial attention is designed, adaptively enhancing the feature expression of important areas and obtaining more discriminative feature vectors. On the other hand, we incorporate attention erasure methods that compel the network to concentrate on less prominent areas, thereby enhancing the network’s robustness. Our method can be seamlessly integrated into various backbone networks. Finally, an evaluation of our approach is conducted across diverse public datasets, accompanied by a comprehensive comparative analysis against state-of-the-art methodologies. The experimental findings substantiate the efficacy and viability of our method in real-world scenarios, exemplifying noteworthy breakthroughs in intricate fine-grained classification endeavors.

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Acknowledgements

This work is partially supported by the Guangxi Science and Technology Project (2021GXNSFBA220035, AD20159034), the Open Funds from Guilin University of Electronic Technology, Guangxi Key Laboratory of Image and Graphic Intelligent Processing (GIIP2208) and the National Natural Science Foundation of China (61962014).

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Authors and Affiliations

  1. Guilin University of Electronic Technology, Guilin, 541004, China

    Dingzhou Xie, Cheng Pang, Guanhua Wu & Rushi Lan

Authors
  1. Dingzhou Xie
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  2. Cheng Pang
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  3. Guanhua Wu
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  4. Rushi Lan
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Corresponding author

Correspondence to Cheng Pang .

Editor information

Editors and Affiliations

  1. Kyushu Institute of Technology, Kitakyushu, Fukuoka, Japan

    Huimin Lu

  2. The University of Sydney, Sydney, NSW, Australia

    Michael Blumenstein

  3. Yonsei University, Seoul, Korea (Republic of)

    Sung-Bae Cho

  4. Chinese Academy of Sciences, Bejing, China

    Cheng-Lin Liu

  5. Osaka University, Osaka, Ibaraki, Japan

    Yasushi Yagi

  6. Kyushu Institute of Technology, Kitakyushu, Japan

    Tohru Kamiya

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Xie, D., Pang, C., Wu, G., Lan, R. (2023). Feature Channel Adaptive Enhancement for Fine-Grained Visual Classification. In: Lu, H., Blumenstein, M., Cho, SB., Liu, CL., Yagi, Y., Kamiya, T. (eds) Pattern Recognition. ACPR 2023. Lecture Notes in Computer Science, vol 14408. Springer, Cham. https://doi.org/10.1007/978-3-031-47665-5_16

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  • DOI: https://doi.org/10.1007/978-3-031-47665-5_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-47664-8

  • Online ISBN: 978-3-031-47665-5

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