First RAG, Second SEG: A Training-Free Paradigm for Camouflaged Object Detection

• URL: http://arxiv.org/abs/2508.15313v2
• Date: Mon, 15 Sep 2025 05:21:07 GMT
• Title: First RAG, Second SEG: A Training-Free Paradigm for Camouflaged Object Detection
• Authors: Wutao Liu, YiDan Wang, Pan Gao,
• Abstract summary: Existing approaches often rely on heavy training and large computational resources.<n>We propose RAG-SEG, a training-free paradigm that decouples COD into two stages: Retrieval-Augmented Generation (RAG) for generating coarse masks as prompts, followed by SAM-based segmentation (SEG) for refinement.<n>RAG-SEG constructs a compact retrieval database via unsupervised clustering, enabling fast and effective feature retrieval.<n>Experiments on benchmark COD datasets demonstrate that RAG-SEG performs on par with or surpasses state-of-the-art methods.
• Score: 14.070196423996045
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Camouflaged object detection (COD) poses a significant challenge in computer vision due to the high similarity between objects and their backgrounds. Existing approaches often rely on heavy training and large computational resources. While foundation models such as the Segment Anything Model (SAM) offer strong generalization, they still struggle to handle COD tasks without fine-tuning and require high-quality prompts to yield good performance. However, generating such prompts manually is costly and inefficient. To address these challenges, we propose \textbf{First RAG, Second SEG (RAG-SEG)}, a training-free paradigm that decouples COD into two stages: Retrieval-Augmented Generation (RAG) for generating coarse masks as prompts, followed by SAM-based segmentation (SEG) for refinement. RAG-SEG constructs a compact retrieval database via unsupervised clustering, enabling fast and effective feature retrieval. During inference, the retrieved features produce pseudo-labels that guide precise mask generation using SAM2. Our method eliminates the need for conventional training while maintaining competitive performance. Extensive experiments on benchmark COD datasets demonstrate that RAG-SEG performs on par with or surpasses state-of-the-art methods. Notably, all experiments are conducted on a \textbf{personal laptop}, highlighting the computational efficiency and practicality of our approach. We present further analysis in the Appendix, covering limitations, salient object detection extension, and possible improvements. \textcolor{blue} {Code: https://github.com/Lwt-diamond/RAG-SEG.}

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