AA-CLIP: Enhancing Zero-shot Anomaly Detection via Anomaly-Aware CLIP AA-CLIP is achieved through a straightforward yet effective two-stage approach: it first creates anomaly-aware text anchors to differentiate normal and abnormal semantics clearly, then aligns patch-level visual features with these anchors for precise anomaly localization
【CVPR 2025】AA-CLIP：基于异常感知CLIP的零样本异常检测增强方法（AA-CLIP） - 知乎 While CLIP shows promise for zero-shot AD tasks due to its strong generalization capabilities, its inherent Anomaly-Unawareness leads to limited discrimination between normal and abnormal features To address this problem, we propose Anomaly-Aware CLIP (AA-CLIP), which enhances CLIP's anomaly discrimination ability in both text and visual
AA-CLIP: Enhancing Zero-Shot Anomaly Detection via Anomaly-Aware CLIP AA-CLIP is achieved by a straightforward and effective two-stage approach: it first creates anomaly-aware text anchors to clearly differentiate normal and abnormal semantics, then aligns patch-level visual features with these anchors for precise anomaly localization AA-CLIP uses lightweight linear residual adapters to maintain CLIP's
AA-CLIP: Enhancing Zero-Shot Anomaly Detection via Anomaly-Aware CLIP To address this problem, we propose Anomaly-Aware CLIP (AA-CLIP), which enhances CLIP’s anomaly discrimination ability in both text and visual spaces while preserving its generalization capability AA-CLIP is achieved through a straightforward yet effective two-stage approach: it first creates anomaly-aware text anchors to differentiate