PromptAD: Learning Prompts with only Normal Samples for Few-Shot Anomaly Detection
Xiaofan Li, Zhizhong Zhang, Xin Tan, Chengwei Chen, Yanyun Qu, Yuan, Xie, Lizhuang Ma

TL;DR
PromptAD introduces a novel one-class prompt learning approach for few-shot industrial anomaly detection, effectively utilizing normal samples and explicit margin control to outperform existing methods in diverse settings.
Contribution
It proposes a new one-class prompt learning method with semantic concatenation and explicit anomaly margin, addressing challenges in anomaly detection without anomaly images.
Findings
Achieves top performance in 11/12 few-shot settings on MVTec and VisA datasets.
Introduces semantic concatenation to generate negative samples for prompt learning.
Utilizes explicit anomaly margin to improve detection accuracy.
Abstract
The vision-language model has brought great improvement to few-shot industrial anomaly detection, which usually needs to design of hundreds of prompts through prompt engineering. For automated scenarios, we first use conventional prompt learning with many-class paradigm as the baseline to automatically learn prompts but found that it can not work well in one-class anomaly detection. To address the above problem, this paper proposes a one-class prompt learning method for few-shot anomaly detection, termed PromptAD. First, we propose semantic concatenation which can transpose normal prompts into anomaly prompts by concatenating normal prompts with anomaly suffixes, thus constructing a large number of negative samples used to guide prompt learning in one-class setting. Furthermore, to mitigate the training challenge caused by the absence of anomaly images, we introduce the concept of…
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Taxonomy
TopicsAnomaly Detection Techniques and Applications · Network Security and Intrusion Detection · Adversarial Robustness in Machine Learning
