  This paper presents a novel method that leverages a visual-language model,
CLIP, as a data source for zero-shot anomaly detection. Tremendous efforts have
been put towards developing anomaly detectors due to their potential industrial
applications. Considering the difficulty in acquiring various anomalous samples
for training, most existing methods train models with only normal samples and
measure discrepancies from the distribution of normal samples during inference,
which requires training a model for each object category. The problem of this
inefficient training requirement has been tackled by designing a CLIP-based
anomaly detector that applies prompt-guided classification to each part of an
image in a sliding window manner. However, the method still suffers from the
labor of careful prompt ensembling with known object categories. To overcome
the issues above, we propose leveraging CLIP as a data source for training. Our
method generates text embeddings with the text encoder in CLIP with typical
prompts that include words of normal and anomaly. In addition to these words,
we insert several randomly generated words into prompts, which enables the
encoder to generate a diverse set of normal and anomalous samples. Using the
generated embeddings as training data, a feed-forward neural network learns to
extract features of normal and anomaly from CLIP's embeddings, and as a result,
a category-agnostic anomaly detector can be obtained without any training
images. Experimental results demonstrate that our method achieves
state-of-the-art performance without laborious prompt ensembling in zero-shot
setups.
