AI RESEARCH
UnGAP: Uncertainty-Guided Affine Prompting for Real-Time Crack Segmentation
arXiv CS.CV
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ArXi:2605.02380v1 Announce Type: new Real-time crack segmentation is vital for structural health monitoring but is plagued by aleatoric uncertainties arising from varying lighting, blur, and texture ambiguity. Current uncertainty-aware approaches typically treat uncertainty estimation as a passive endpoint for post-hoc analysis, failing to close the loop by feeding this information back to refine feature representations.