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Fusion Boundary and Gradient Enhancement Network for Camouflage Object Detection

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MultiMedia Modeling (MMM 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14555))

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Abstract

The problems of boundary interruption and missing internal texture feature have not been well solved in the current camouflaged object detection model, and the parameters of the model are generally large. To overcome these challenges, we propose a fusion boundary and gradient enhancement networks BGENet, which guides the context features by gradient features and boundary features together. BGENet is divided into three branches, context feature branch, boundary feature branch and gradient feature branch. Furthermore, a parallel context information enhancement module is introduced to enhance the context features. The designed pre-background information interaction module is used to highlight the boundary features of the camouflaged object and guide the context features to compensate for the boundary breaks in the context features, while we use the learned gradient features to guide the context features through the proposed gradient guidance module, and enhances internal information about context features. Experiments on CAMO, COD10K and NC4K three datasets confirm the effectiveness of our BGENet, which uses only 20.81M parameters and achieves superior performance compared with traditional and SOTA methods.

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Acknowledgements

This work is supported by the Inner Mongolia Science and Technology Project No.2021GG0166.

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Authors and Affiliations

  1. Inner Mongolia University, Hohhot, China

    Guangrui Liu & Wei Wu

Authors
  1. Guangrui Liu
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  2. Wei Wu
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Corresponding author

Correspondence to Wei Wu .

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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Liu, G., Wu, W. (2024). Fusion Boundary and Gradient Enhancement Network for Camouflage Object Detection. In: Rudinac, S., et al. MultiMedia Modeling. MMM 2024. Lecture Notes in Computer Science, vol 14555. Springer, Cham. https://doi.org/10.1007/978-3-031-53308-2_14

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  • DOI: https://doi.org/10.1007/978-3-031-53308-2_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-53307-5

  • Online ISBN: 978-3-031-53308-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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