  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
https://github.com/zqhang/AnomalyCLIP.
