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<p>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 <a href="https://github.com/HVision-NKU/AdaptCOD.">https://github.com/HVision-NKU/AdaptCOD.</a></p>

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Camouflaged Object Detection with Adaptive Partition and Background Retrieval

  • 期刊论文

  • 发表日期：

    2025年3月22日

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  • Bowen Yin,
  • Xuying Zhang,
  • Li Liu,
  • Ming-Ming Cheng,
  • Yongxiang Liu,
  • Qibin Hou

摘要

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.