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
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
explicit anomaly margin, which is used to explicitly control the margin between
normal prompt features and anomaly prompt features through a hyper-parameter.
For image-level/pixel-level anomaly detection, PromptAD achieves first place in
11/12 few-shot settings on MVTec and VisA.