Max pooling is the de facto standard for converting anomaly score maps into image-level decisions in memory-bank-based unsupervised anomaly detection (UAD). However, because it relies on a single extreme response, it discards most information about how anomaly evidence is distributed and structured across the image, often causing normal and anomalous scores to overlap. We propose StructCore, a training-free, structure-aware image-level scoring method that goes beyond max pooling. Given an anomaly score map, StructCore computes a low-dimensional structural descriptor phi(S) that captures distributional and spatial characteristics, and refines image-level scoring via a diagonal Mahalanobis calibration estimated from train-good samples, without modifying pixel-level localization. StructCore achieves image-level AUROC scores of 99.6% on MVTec AD and 98.4% on VisA, demonstrating robust image-level anomaly detection by exploiting structural signatures missed by max pooling.

翻译：在基于记忆库的无监督异常检测中，最大池化是将异常分数图转换为图像级决策的事实标准。然而，由于其依赖于单一极端响应，它丢弃了关于异常证据在图像中如何分布和结构化的大部分信息，常常导致正常与异常分数重叠。我们提出StructCore，一种超越最大池化的免训练、结构感知图像级评分方法。给定一个异常分数图，StructCore计算一个低维结构描述符phi(S)，该描述符捕捉分布和空间特征，并通过从训练正常样本估计的对角马氏距离校准来优化图像级评分，而无需修改像素级定位。StructCore在MVTec AD上实现了99.6%的图像级AUROC分数，在VisA上实现了98.4%，通过利用被最大池化忽略的结构特征，展示了鲁棒的图像级异常检测能力。