GFR-SAM: Training-Free Referring Camouflaged Object Segmentation via Cross-Image Prompting

• URL: http://arxiv.org/abs/2607.11732v1
• Date: Mon, 13 Jul 2026 15:55:45 GMT
• Title: GFR-SAM: Training-Free Referring Camouflaged Object Segmentation via Cross-Image Prompting
• Abstract summary: Referring Camouflaged Object Detection (Ref-COD) requires segmenting hidden targets guided by reference cues.<n>We propose GFR-SAM, a robust three-stage training-free framework.<n>First, we introduce In-Context Exemplar-guided exemplars, empowering SAM3 with cross-image inference to generate candidate masks.<n>Second, a Region-Global Contrastive Filtering module ranks candidates through DINOv3-based prototypical alignment.<n>Third, a Geometric-Semantic Refinement module synergizes bounding box and text prompts to recover fine-grained boundaries and enhance instance recall.
• Score: 40.66340261994875
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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 through DINOv3-based prototypical alignment, effectively suppressing background distractors. Finally, a Geometric-Semantic Refinement module synergizes bounding box and text prompts to recover fine-grained boundaries and enhance instance recall. Evaluated on the R2C7K benchmark, GFR-SAM outperforms existing training-free methods by 8.7\% in weighted F-measure ($F_β^w$) and competes with supervised state-of-the-art counterparts. Ultimately, this work underscores the potential of unlocking SAM3's latent capability for cross-image In-Context prompting, establishing a robust, training-free paradigm that effectively bridges the gap between general-purpose foundation models and specialized, label-intensive perception tasks without the need for task-specific fine-tuning.

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