AA-CLIP: Enhancing Zero-shot Anomaly Detection via Anomaly-Aware CLIP

• URL: http://arxiv.org/abs/2503.06661v1
• Date: Sun, 09 Mar 2025 15:22:52 GMT
• Title: AA-CLIP: Enhancing Zero-shot Anomaly Detection via Anomaly-Aware CLIP
• Authors: Wenxin Ma, Xu Zhang, Qingsong Yao, Fenghe Tang, Chenxu Wu, Yingtai Li, Rui Yan, Zihang Jiang, S. Kevin Zhou,
• Abstract summary: Anomaly detection (AD) identifies outliers for applications like defect and lesion detection.<n>We propose Anomaly-Aware CLIP (AA-CLIP), which enhances CLIP's anomaly discrimination ability in both text and visual spaces.<n>AA-CLIP is achieved through a straightforward yet effective two-stage approach.
• Score: 33.213400694016
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Anomaly detection (AD) identifies outliers for applications like defect and lesion detection. 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 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 normal and abnormal semantics clearly, then aligns patch-level visual features with these anchors for precise anomaly localization. This two-stage strategy, with the help of residual adapters, gradually adapts CLIP in a controlled manner, achieving effective AD while maintaining CLIP's class knowledge. Extensive experiments validate AA-CLIP as a resource-efficient solution for zero-shot AD tasks, achieving state-of-the-art results in industrial and medical applications. The code is available at https://github.com/Mwxinnn/AA-CLIP.

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