Iterative Refinement of Semantic and Spatial Representations for Open-Vocabulary Camouflaged Object Segmentation

Open-Vocabulary Camouflaged Object Segmentation aimsto segment camouflaged objects from unseen categories. Existing two-stage methods typically treat category semantics as a one-time priorfor segmentation, thereby lacking deep and iterative interaction betweentextual semantics and visual spatial structures. To address this limita-tion, we propose an iterative two-stage refinement framework based onsemantic context and spatial structure. Specifically, we first design aspatial-structure-aware category re-ranker that leverages segmentationmasks generated by SAM2 to reorder CLIP’s candidate categories ac-cording to region-level visual consistency. Furthermore, we introduce asemantic-context-based segmentation modulator that injects the refinedcategory information into SAM2, guiding SAM2 to produce more preciseand discriminative segmentation results. Notably, the proposed re-rankerand modulator are jointly optimized in an iterative manner, forming aclosed-loop refinement process that enables mutual guidance between se-mantic representations and spatial features. Through iterative updates,semantic context and spatial structure are progressively enhanced. Ex-tensive experiments demonstrate that the proposed method significantlyoutperforms existing open-vocabulary methods and camouflaged objectsegmentation approaches. In particular, the proposed method achieves a14.4% improvement in the cSm metric compared with OVCoser.