This paper studies zero-shot anomaly classification (AC) and segmentation
(AS) in industrial vision. We reveal that the abundant normal and abnormal cues
implicit in unlabeled test images can be exploited for anomaly determination,
which is ignored by prior methods. Our key observation is that for the
industrial product images, the normal image patches could find a relatively
large number of similar patches in other unlabeled images, while the abnormal
ones only have a few similar patches. We leverage such a discriminative
characteristic to design a novel zero-shot AC/AS method by Mutual Scoring
(MuSc) of the unlabeled images, which does not need any training or prompts.
Specifically, we perform Local Neighborhood Aggregation with Multiple Degrees
(LNAMD) to obtain the patch features that are capable of representing anomalies
in varying sizes. Then we propose the Mutual Scoring Mechanism (MSM) to
leverage the unlabeled test images to assign the anomaly score to each other.
Furthermore, we present an optimization approach named Re-scoring with
Constrained Image-level Neighborhood (RsCIN) for image-level anomaly
classification to suppress the false positives caused by noises in normal
images. The superior performance on the challenging MVTec AD and VisA datasets
demonstrates the effectiveness of our approach. Compared with the
state-of-the-art zero-shot approaches, MuSc achieves a $\textbf{21.1%}$ PRO
absolute gain (from 72.7% to 93.8%) on MVTec AD, a $\textbf{19.4%}$ pixel-AP
gain and a $\textbf{14.7%}$ pixel-AUROC gain on VisA. In addition, our
zero-shot approach outperforms most of the few-shot approaches and is
comparable to some one-class methods. Code is available at
this https URL

本研究探讨在工业视觉中的零样本异常分类和分割，通过对未标记测试图像中丰富的正常和异常线索进行利用，通过互相评分机制（MuSc）设计了一种不需要任何训练或提示的新型零样本异常分类 / 分割方法，并通过局部邻域聚合和多个程度的 Local Neighborhood Aggregation（LNAMD）获得能够表示不同尺寸异常的图像块特征.

MuSc：无标签图像的零样本工业异常分类与分割

MuSc: Zero-Shot Industrial Anomaly Classification and Segmentation with  Mutual Scoring of the Unlabeled Images

In industrial vision, the anomaly detection problem can be addressed with an
autoencoder trained to map an arbitrary image, i.e. with or without any defect,
to a clean image, i.e. without any defect. In this approach, anomaly detection
relies conventionally on the reconstruction residual or, alternatively, on the
reconstruction uncertainty. To improve the sharpness of the reconstruction, we
consider an autoencoder architecture with skip connections. In the common
scenario where only clean images are available for training, we propose to
corrupt them with a synthetic noise model to prevent the convergence of the
network towards the identity mapping, and introduce an original Stain noise
model for that purpose. We show that this model favors the reconstruction of
clean images from arbitrary real-world images, regardless of the actual defects
appearance. In addition to demonstrating the relevance of our approach, our
validation provides the first consistent assessment of reconstruction-based
methods, by comparing their performance over the MVTec AD dataset, both for
pixel- and image-wise anomaly detection.

本文研究工业视觉中基于自编码器网络的异常检测问题，通过引入 skip 连接的自编码器结构并使用一种新的 Stain 噪声模型，使网络能从有缺陷的图像中重建出干净图像，实现像素级或者图像级的异常检测。