ROADS adds class-aware prompt tokens to a reverse-distillation student-teacher network and aligns AdaIN style codes between source and target domains, improving multi-class anomaly detection and localization under domain shift.
PaDiM: a patch distribution modeling framework for anomaly detection and localization
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ROADS: Robust Prompt-driven Multi-Class Anomaly Detection under Domain Shift
ROADS adds class-aware prompt tokens to a reverse-distillation student-teacher network and aligns AdaIN style codes between source and target domains, improving multi-class anomaly detection and localization under domain shift.