A Simple yet Powerful Instance-Aware Prompting Framework for Training-free Camouflaged Object Segmentation (2508.06904v1)

Abstract: Camouflaged Object Segmentation (COS) remains highly challenging due to the intrinsic visual similarity between target objects and their surroundings. While training-based COS methods achieve good performance, their performance degrades rapidly with increased annotation sparsity. To circumvent this limitation, recent studies have explored training-free COS methods, leveraging the Segment Anything Model (SAM) by automatically generating visual prompts from a single task-generic prompt (\textit{e.g.}, "\textit{camouflaged animal}") uniformly applied across all test images. However, these methods typically produce only semantic-level visual prompts, causing SAM to output coarse semantic masks and thus failing to handle scenarios with multiple discrete camouflaged instances effectively. To address this critical limitation, we propose a simple yet powerful \textbf{I}nstance-\textbf{A}ware \textbf{P}rompting \textbf{F}ramework (IAPF), the first training-free COS pipeline that explicitly converts a task-generic prompt into fine-grained instance masks. Specifically, the IAPF comprises three steps: (1) Text Prompt Generator, utilizing task-generic queries to prompt a Multimodal LLM (MLLM) for generating image-specific foreground and background tags; (2) \textbf{Instance Mask Generator}, leveraging Grounding DINO to produce precise instance-level bounding box prompts, alongside the proposed Single-Foreground Multi-Background Prompting strategy to sample region-constrained point prompts within each box, enabling SAM to yield a candidate instance mask; (3) Self-consistency Instance Mask Voting, which selects the final COS prediction by identifying the candidate mask most consistent across multiple candidate instance masks. Extensive evaluations on standard COS benchmarks demonstrate that the proposed IAPF significantly surpasses existing state-of-the-art training-free COS methods.