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

TL;DR

AnomalyCLIP leverages large vision-language models by learning object-agnostic prompts to improve zero-shot anomaly detection across diverse domains, focusing on abnormal regions rather than object semantics.

Contribution

It introduces a novel prompt learning method that enables CLIP to detect anomalies without training data, emphasizing abnormality recognition across varied object classes.

Findings

Outperforms existing zero-shot anomaly detection methods on 17 datasets.

Effective in diverse domains including defect inspection and medical imaging.

Achieves state-of-the-art zero-shot segmentation of anomalies.

Abstract

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.