GFR-SAM: Training-Free Referring Camouflaged Object Segmentation via Cross-Image Prompting
Referring Camouflaged Object Detection (Ref-COD) requires segmenting hidden targets guided by reference cues. While supervised methods are annotation-heavy and training-free approaches via sparse point-prompting are sensitive to localization errors, we propose GFR-SAM, a robust three-stage training-free framework. GFR-SAM shifts the paradigm from fragile point-matching to a "Generate-Filter-Refine" pipeline. First, we introduce In-Context Exemplar-guided Segmentation, empowering SAM3 with cross-image inference to generate candidate masks via holistic visual exemplars, bypassing its native intra-image constraints. Second, a Region-Global Contrastive Filtering module ranks candidates throug...
| Authors | Yilong Yang, Jianxin Tian, Shengchuan Zhang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 13 July 2026 |
| Last updated at source | 13 July 2026 |
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