PA-CLIP：通过伪异常感知增强零样本异常检测
PA-CLIP: Enhancing Zero-Shot Anomaly Detection through Pseudo-Anomaly Awareness
AI总结：
PA-CLIP提出一种基于伪异常的零样本异常检测方法，通过多尺度特征聚合、双记忆库和决策模块减少背景噪声与误报，在MVTec AD和VisA数据集上优于现有零样本方法。
摘要：
在工业异常检测（IAD）中，在不同异常和不同成像条件下准确识别缺陷仍然是一个重大挑战。传统方法通常面临高误报率，经常将正常阴影和表面变形误判为缺陷，这一问题在具有复杂和精细表面特征的产品中尤为明显。为了解决这些挑战，我们引入了PA-CLIP，这是一种零样本异常检测方法，通过基于伪异常的框架减少背景噪声并增强缺陷检测。所提出的方法集成了多尺度特征聚合策略以捕获详细的全局和局部信息，两个记忆库用于区分背景信息（包括正常模式和伪异常）与真实异常特征，以及一个决策模块，旨在最小化由环境变化引起的误报，同时保持高缺陷敏感度。在MVTec AD和VisA数据集上的演示表明，PA-CLIP优于现有的零样本方法，为工业缺陷检测提供了稳健的解决方案。
英文摘要：
In industrial anomaly detection (IAD), accurately identifying defects amidst diverse anomalies and under varying imaging conditions remains a significant challenge. Traditional approaches often struggle with high false-positive rates, frequently misclassifying normal shadows and surface deformations as defects, an issue that becomes particularly pronounced in products with complex and intricate surface features. To address these challenges, we introduce PA-CLIP, a zero-shot anomaly detection method that reduces background noise and enhances defect detection through a pseudo-anomaly-based framework. The proposed method integrates a multiscale feature aggregation strategy for capturing detailed global and local information, two memory banks for distinguishing background information, including normal patterns and pseudo-anomalies, from true anomaly features, and a decision-making module designed to minimize false positives caused by environmental variations while maintaining high defect sensitivity. Demonstrated on the MVTec AD and VisA datasets, PA-CLIP outperforms existing zero-shot methods, providing a robust solution for industrial defect detection.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
