  Recently, large-scale vision-language models such as CLIP have demonstrated
immense potential in zero-shot anomaly segmentation (ZSAS) task, utilizing a
unified model to directly detect anomalies on any unseen product with
painstakingly crafted text prompts. However, existing methods often assume that
the product category to be inspected is known, thus setting product-specific
text prompts, which is difficult to achieve in the data privacy scenarios.
Moreover, even the same type of product exhibits significant differences due to
specific components and variations in the production process, posing
significant challenges to the design of text prompts. In this end, we propose a
visual context prompting model (VCP-CLIP) for ZSAS task based on CLIP. The
insight behind VCP-CLIP is to employ visual context prompting to activate
CLIP's anomalous semantic perception ability. In specific, we first design a
Pre-VCP module to embed global visual information into the text prompt, thus
eliminating the necessity for product-specific prompts. Then, we propose a
novel Post-VCP module, that adjusts the text embeddings utilizing the
fine-grained features of the images. In extensive experiments conducted on 10
real-world industrial anomaly segmentation datasets, VCP-CLIP achieved
state-of-the-art performance in ZSAS task. The code is available at
https://github.com/xiaozhen228/VCP-CLIP.
