EntroAD: Structural Entropy-Guided Prompt Adaptation for Zero-Shot Anomaly Detection 事件

EntroAD: Structural Entropy-Guided Prompt Adaptation for Zero-Shot Anomaly Detection arXiv:2605.28630v1 Announce Type: new Abstract: Zero-Shot Anomaly Detection (ZSAD) aims to detect anomalies in unseen domains without target-domain adaptation. Recent CLIP-based methods have shown promising performance by leveraging prompt learning and visual-text alignment. However, most existing approaches rely on a single adaptation pathway, which may be insufficient for heterogeneous anomaly patterns across