Zero-shot anomaly detection (ZSAD) requires detection models trained using
auxiliary data to detect anomalies without any training sample in a target
dataset. It is a crucial task when training data is not accessible due to
various concerns, \eg, data privacy, yet it is challenging since the models
need to generalize to anomalies across different domains where the appearance
of foreground objects, abnormal regions, and background features, such as
defects/tumors on different products/organs, can vary significantly. Recently
large pre-trained vision-language models (VLMs), such as CLIP, have
demonstrated strong zero-shot recognition ability in various vision tasks,
including anomaly detection. However, their ZSAD performance is weak since the
VLMs focus more on modeling the class semantics of the foreground objects
rather than the abnormality/normality in the images. In this paper we introduce
a novel approach, namely AnomalyCLIP, to adapt CLIP for accurate ZSAD across
different domains. The key insight of AnomalyCLIP is to learn object-agnostic
text prompts that capture generic normality and abnormality in an image
regardless of its foreground objects. This allows our model to focus on the
abnormal image regions rather than the object semantics, enabling generalized
normality and abnormality recognition on diverse types of objects. Large-scale
experiments on 17 real-world anomaly detection datasets show that AnomalyCLIP
achieves superior zero-shot performance of detecting and segmenting anomalies
in datasets of highly diverse class semantics from various defect inspection
and medical imaging domains. Code will be made available at
this https URL

AnomalyCLIP 通过学习无关物体的文本提示来捕捉图像中的通常和异常，从而实现了对高度多样的物体数据集进行广义正常性和异常性识别的零样本卓越性能。

AnomalyCLIP: 面向对象无关的零样本异常检测的提示学习

AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly  Detection

The human visual system can spot an abnormal image, and reason about what
makes it strange. This task has not received enough attention in computer
vision. In this paper we study various types of atypicalities in images in a
more comprehensive way than has been done before. We propose a new dataset of
abnormal images showing a wide range of atypicalities. We design human subject
experiments to discover a coarse taxonomy of the reasons for abnormality. Our
experiments reveal three major categories of abnormality: object-centric,
scene-centric, and contextual. Based on this taxonomy, we propose a
comprehensive computational model that can predict all different types of
abnormality in images and outperform prior arts in abnormality recognition.

本文研究了在计算机视觉中，如何利用人类视觉系统对图像异常性进行识别和分类，提出了一种基于人类实验数据的异常图像分类法以及一种能够准确预测各种异常情况的计算模型，并相应建立了一个异常图像数据集。