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 intr
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- PossiblePossibly related (embedding) · 50%NVIDIA-ISAAC-ROS/isaac_ros_object_detection →
- LinkedLinked via arxiv author · 85%Yilong Yang →
“GFR-SAM: Training-Free Referring Camouflaged Object Segmentation via Cross-Image Prompting”
- LinkedLinked via arxiv author · 85%Jianxin Tian →
“GFR-SAM: Training-Free Referring Camouflaged Object Segmentation via Cross-Image Prompting”
- LinkedLinked via arxiv author · 85%Shengchuan Zhang →
“GFR-SAM: Training-Free Referring Camouflaged Object Segmentation via Cross-Image Prompting”
- LinkedLinked via arxiv author · 85%Liujuan Cao →
“GFR-SAM: Training-Free Referring Camouflaged Object Segmentation via Cross-Image Prompting”