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Camouflaged Object Detection with Adaptive Partition and Background Retrieval.

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Title: Camouflaged Object Detection with Adaptive Partition and Background Retrieval.
Authors: Yin, Bowen1 (AUTHOR), Zhang, Xuying1 (AUTHOR), Liu, Li2 (AUTHOR) liuli_nudt@nudt.edu.cn, Cheng, Ming-Ming1 (AUTHOR), Liu, Yongxiang2 (AUTHOR), Hou, Qibin1 (AUTHOR) andrewhoux@gmail.com
Source: International Journal of Computer Vision. Jul2025, Vol. 133 Issue 7, p4877-4893. 17p.
Subjects: Object recognition (Computer vision), Simplicity
Abstract: Recent works confirm the importance of local details for identifying camouflaged objects. However, how to identify the details around the target objects via background cues lacks in-depth study. In this paper, we take this into account and present a novel learning framework for camouflaged object detection, called AdaptCOD. To be specific, our method decouples the detection process into three parts, namely localization, segmentation, and retrieval. We design a context adaptive partition strategy to dynamically select a reasonable context region for local segmentation and a background retrieval module to further polish the camouflaged object boundaries. Despite the simplicity, our method enables even a simple COD model to achieve great performance. Extensive experiments show that AdaptCOD surpasses all existing state-of-the-art methods on three widely-used camouflaged object detection benchmarks. Code is publicly available at https://github.com/HVision-NKU/AdaptCOD. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Computer Vision is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Camouflaged Object Detection with Adaptive Partition and Background Retrieval.
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Computer+Vision%22">International Journal of Computer Vision</searchLink>. Jul2025, Vol. 133 Issue 7, p4877-4893. 17p.
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  Data: <searchLink fieldCode="DE" term="%22Object+recognition+%28Computer+vision%29%22">Object recognition (Computer vision)</searchLink><br /><searchLink fieldCode="DE" term="%22Simplicity%22">Simplicity</searchLink>
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  Label: Abstract
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  Data: Recent works confirm the importance of local details for identifying camouflaged objects. However, how to identify the details around the target objects via background cues lacks in-depth study. In this paper, we take this into account and present a novel learning framework for camouflaged object detection, called AdaptCOD. To be specific, our method decouples the detection process into three parts, namely localization, segmentation, and retrieval. We design a context adaptive partition strategy to dynamically select a reasonable context region for local segmentation and a background retrieval module to further polish the camouflaged object boundaries. Despite the simplicity, our method enables even a simple COD model to achieve great performance. Extensive experiments show that AdaptCOD surpasses all existing state-of-the-art methods on three widely-used camouflaged object detection benchmarks. Code is publicly available at https://github.com/HVision-NKU/AdaptCOD. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of International Journal of Computer Vision is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s11263-025-02406-6
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      – Code: eng
        Text: English
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        PageCount: 17
        StartPage: 4877
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      – SubjectFull: Object recognition (Computer vision)
        Type: general
      – SubjectFull: Simplicity
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      – TitleFull: Camouflaged Object Detection with Adaptive Partition and Background Retrieval.
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            – D: 01
              M: 07
              Text: Jul2025
              Type: published
              Y: 2025
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            – TitleFull: International Journal of Computer Vision
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