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Foreground Feature Enhancement and Peak & Background Suppression for Fine-Grained Visual Classification

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MultiMedia Modeling (MMM 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14554))

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Abstract

Fine-grained visual classification task is challenging due to large variation in the appearance of the same subcategories and similarity in the appearance of different subcategories. To tackle the challenges, locating multiple discriminative regions plays a critical role. However, most previous works ignore the impact of background, which may provide negative clues that are not necessary or harmful for the network to classification. In this paper, we propose Foreground Feature Enhancement (FFE) module and Peak & Background Suppression (PBS) module, which are inserted in different layers of the CNN. The FFE module is designed to enhance and extract the most discriminative feature in the feature maps, and the PBS module is employed to suppress the peak features and background noise in the feature maps, forcing the network to mine other equally important features. Our proposed method can be trained end-to-end and does not require bounding boxes/part annotations. The experimental results achieve competitive performances on CUB200-2011, FGVC Aircraft, and Stanford Cars datasets.

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Authors and Affiliations

  1. School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang, China

    Die Yu, Zhaoyan Fang & Yong Jiang

Authors
  1. Die Yu
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  2. Zhaoyan Fang
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  3. Yong Jiang
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Corresponding author

Correspondence to Yong Jiang .

Editor information

Editors and Affiliations

  1. University of Amsterdam, Amsterdam, The Netherlands

    Stevan Rudinac

  2. Delft University of Technology, Delft, The Netherlands

    Alan Hanjalic

  3. Delft University of Technology, Delft, The Netherlands

    Cynthia Liem

  4. University of Amsterdam, Amsterdam, The Netherlands

    Marcel Worring

  5. Reykjavik University, Reykjavik, Iceland

    Björn Þór Jónsson

  6. Microsoft Research Lab – Asia, Beijing, China

    Bei Liu

  7. The University of Tokyo, Tokyo, Japan

    Yoko Yamakata

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© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Yu, D., Fang, Z., Jiang, Y. (2024). Foreground Feature Enhancement and Peak & Background Suppression for Fine-Grained Visual Classification. In: Rudinac, S., et al. MultiMedia Modeling. MMM 2024. Lecture Notes in Computer Science, vol 14554. Springer, Cham. https://doi.org/10.1007/978-3-031-53305-1_11

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  • DOI: https://doi.org/10.1007/978-3-031-53305-1_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-53304-4

  • Online ISBN: 978-3-031-53305-1

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