{"ID":2884343,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.06904","arxiv_id":"2508.06904","title":"An Instance-Aware Prompting Framework for Training-free Camouflaged Object Segmentation","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.","short_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 str...","url_abs":"https://arxiv.org/abs/2508.06904","url_pdf":"https://arxiv.org/pdf/2508.06904v3","authors":"[\"Chao Yin\",\"Jide Li\",\"Hang Yao\",\"Xiaoqiang Li\"]","published":"2025-08-09T09:35:32Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}