AFR-CLIP：通过无状态到有状态的异常特征校正增强零样本工业异常检测
AFR-CLIP: Enhancing Zero-Shot Industrial Anomaly Detection with Stateless-to-Stateful Anomaly Feature Rectification
AI总结：
本文提出AFR-CLIP框架，通过图像引导的无状态到有状态文本特征校正，结合自提示和多块特征聚合模块，在无需目标样本训练的情况下提升零样本工业与医学异常检测性能。
摘要：
近年来，零样本异常检测（ZSAD）已成为工业检测和医学诊断中的关键范式，能够在训练期间无需任何目标数据集样本的情况下检测新物体中的缺陷。现有的基于CLIP的ZSAD方法通过测量视觉特征与文本特征之间的余弦相似度来生成异常图。然而，CLIP与物体类别而非其异常状态的对齐限制了其在异常检测中的有效性。为解决这一局限性，我们提出了AFR-CLIP，一个基于CLIP的异常特征校正框架。AFR-CLIP首先执行图像引导的文本校正，将图像中的隐式缺陷信息嵌入到一个无状态提示中，该提示描述物体类别而不指示任何异常状态。随后，将丰富的文本嵌入与两个预定义的有状态（正常或异常）嵌入进行比较，其文本间相似度生成突出缺陷区域的异常图。为进一步增强对多尺度特征和复杂异常的感知能力，我们引入了自提示（SP）和多块特征聚合（MPFA）模块。在工业和医学领域的十一个异常检测基准上进行了大量实验，证明了AFR-CLIP在ZSAD中的优越性。
英文摘要：
Recently, zero-shot anomaly detection (ZSAD) has emerged as a pivotal paradigm for industrial inspection and medical diagnostics, detecting defects in novel objects without requiring any target-dataset samples during training. Existing CLIP-based ZSAD methods generate anomaly maps by measuring the cosine similarity between visual and textual features. However, CLIP's alignment with object categories instead of their anomalous states limits its effectiveness for anomaly detection. To address this limitation, we propose AFR-CLIP, a CLIP-based anomaly feature rectification framework. AFR-CLIP first performs image-guided textual rectification, embedding the implicit defect information from the image into a stateless prompt that describes the object category without indicating any anomalous state. The enriched textual embeddings are then compared with two pre-defined stateful (normal or abnormal) embeddings, and their text-on-text similarity yields the anomaly map that highlights defective regions. To further enhance perception to multi-scale features and complex anomalies, we introduce self prompting (SP) and multi-patch feature aggregation (MPFA) modules. Extensive experiments are conducted on eleven anomaly detection benchmarks across industrial and medical domains, demonstrating AFR-CLIP's superiority in ZSAD.
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。
