SGPer combines DINOv2 semantic priors converted to dense prompts with SAM geometric priors through disease-sensitive adapters and dynamic consistency filtering to deliver robust limited-data wheat disease segmentation.
Chapman, and Zi Huang
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Learning to Synergize Semantic and Geometric Priors for Limited-Data Wheat Disease Segmentation
SGPer combines DINOv2 semantic priors converted to dense prompts with SAM geometric priors through disease-sensitive adapters and dynamic consistency filtering to deliver robust limited-data wheat disease segmentation.