PASR: Pattern-Aware Scene-Conditioned Reasoning for Camouflaged Object Detection

Camouflaged Object Detection (COD) remains challengingdue to the deliberate alignment of foreground statistics with surround-ing background patterns, which induces strong scene-dependent ambi-guity. Existing approaches typically adopt object-centric modeling orreference-based augmentation. They often treat object appearance andbackground statistics independently, without explicitly modeling theirconditional relationship. This work treats COD as a scene-conditionedpattern-deviation reasoning problem and captures how a camouflagedobject deviates from its background scene. In this direction, we proposea pattern-aware scene-conditioned reasoning (PASR) method with twostages. The first stage constructs a scene-aligned background anchor viabackground-first retrieval, while the second stage performs a conditionedpatch-level deviation reasoning. A reference prototype library is con-structed in an offline and annotation-free manner, where foreground pro-totypes are derived from self-inferred rough masks rather than manuallycurated annotations. Unlike retraining-based paradigms, PASR achievesgeneralization through prototype-level expansion rather than parameteradaptation, enabling scalable inference across diverse background dis-tributions. The resulting deviation maps can serve as priors for genericsegmentation models. Extensive experiments on challenging COD bench-marks demonstrate that PASR outperforms existing unsupervised andweakly supervised methods and significantly narrows the gap with fullysupervised models.