BAPNet: Boundary and Ambiguity Priors for Interactive Camouflaged Object Segmentation

Camouflaged object segmentation involves strong foreground–background similarity and ambiguous boundaries, making pixel-level annotation costly and time-consuming. Interactive segmentation offers an efficient solution by using limited user interactions, such as clicks, to assist mask generation. However, existing methods are mainly designed for natural images and often struggle in camouflaged scenarios, where ambiguous boundaries and high prediction uncertainty can lead to inaccurate contours and foreground–background confusion. To address these challenges, we propose a Boundary and Ambiguity Priors for Interactive Camouflaged Object Segmentation (BAPNet) that jointly models boundary information and prediction uncertainty. Specifically, a Grassmann Boundary Guidance Module (GBGM) is introduced to explicitly predict object boundaries and guide the model to focus on foreground–background transition regions, while an Ambiguity-Prior Refinement Module (APRM) identifies potential areas with unreliable predictions and enhances local discrimination in ambiguous regions. To the best of our knowledge, BAPNet is the first systematic study of interactive camouflaged object segmentation. Extensive experiments on multiple camouflaged object datasets demonstrate that the proposed method achieves superior segmentation accuracy and interaction efficiency compared with existing approaches.