An Instance-Aware Prompting Framework for Training-free Camouflaged Object Segmentation

TL;DR

This paper introduces IAPF, a novel training-free framework that enhances camouflaged object segmentation by generating precise instance-level prompts, significantly improving segmentation accuracy without additional training.

Contribution

The paper proposes the first training-free COS method with instance-aware prompting, utilizing an instance mask generator and a novel prompt sampling strategy to improve segmentation granularity.

Findings

Achieves state-of-the-art results on multiple benchmarks.

Effectively handles multiple discrete camouflaged instances.

Maintains high performance without task-specific training.

Abstract

Training-free Camouflaged Object Segmentation (COS) seeks to segment camouflaged objects without task-specific training, by automatically generating visual prompts to guide the Segment Anything Model (SAM). However, existing pipelines mostly yield semantic-level prompts, which drive SAM to coarse semantic masks and struggle to handle multiple discrete camouflaged instances effectively. To address this critical limitation, we propose an \textbf{I}nstance-\textbf{A}ware \textbf{P}rompting \textbf{F}ramework (IAPF) tailored for the first training-free COS that upgrades prompt granularity from semantic to instance-level while keeping all components frozen. The centerpiece is an Instance Mask Generator that (i) leverages a detector-agnostic enumerator to produce precise instance-level box prompts for the foreground tag, and (ii) introduces the Single-Foreground Multi-Background Prompting (SFMBP) strategy to sample region-constrained point prompts within each box prompt, enabling SAM to output instance masks. The pipeline is supported by a simple text prompt generator that produces image-specific tags and a self-consistency vote across synonymous task-generic prompts to stabilize inference. Extensive evaluations on three COS benchmarks, two CIS benchmarks, and two downstream datasets demonstrate state-of-the-art performance among training-free methods. Code will be released upon acceptance.