Camouflaged Object Detection with Adaptive Partition and Background Retrieval.
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| Title: | Camouflaged Object Detection with Adaptive Partition and Background Retrieval. |
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| 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] |
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| Database: | Engineering Source |
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| 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] |
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| ISSN: | 09205691 |
| DOI: | 10.1007/s11263-025-02406-6 |
