PoEM: Reference-Guided Camouflaged Object Detection via Probabilistic Environment Modeling

Reference-guided camouflaged object detection (RGCOD) aims to localize camouflaged objects in a query image using reference information. Existing approaches predominantly formulate RGCOD as feature matching: text-based methods are constrained by the cross-modal semantic gap, whereas vision-based methods typically rely on paired images with pixel-level annotations, limiting scalability and generalization. We propose **PoEM**, a training-free framework that recasts RGCOD as **P**r**o**babilistic **E**nvironment **M**odeling, requiring only target-free reference environment images and no paired annotations. The central idea is to estimate the query image’s background distribution from reference environments and identify camouflaged objects as regions that deviate from this distribution. To this end, we introduce a Describe–Sample–Filter (DSF) agent that automatically constructs high-quality reference environment images that are semantically and structurally aligned with the query image. We then use probabilistic principal component analysis (PPCA) to model the reference environment distribution in DINOv3 feature space and score each query patch by its negative log-likelihood under the resulting model. Higher scores indicate lower compatibility with the reference environment, providing cues for camouflaged object localization. When integrated with the Segment Anything Model (SAM), PoEM consistently outperforms state-of-the-art SAM-based RGCOD methods across multiple benchmarks.