VCP-CLIP: A visual context prompting model for zero-shot anomaly segmentation
Zhen Qu, Xian Tao, Mukesh Prasad, Fei Shen, Zhengtao Zhang, Xinyi Gong, Guiguang Ding
- 📖 Introduction
- 🔧 Environments
- 📊 Data Preparation
- 🚀 Run Experiments
- 🔗 Citation
- 🙏 Acknowledgements
- 📜 License
This repository contains source code for VCP-CLIP implemented with PyTorch.
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.
Create a new conda environment and install required packages.
conda create -n VCP_env python=3.9
conda activate VCP_env
conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 pytorch-cuda=11.7 -c pytorch -c nvidia
Experiments are conducted on a NVIDIA RTX 3090.
Please cite the following paper if the code help your project:
@article{qu2024vcpclipvisualcontextprompting,
title={VCP-CLIP: A visual context prompting model for zero-shot anomaly segmentation},
author={Zhen Qu and Xian Tao and Mukesh Prasad and Fei Shen and Zhengtao Zhang and Xinyi Gong and Guiguang Ding},
year={2024},
eprint={2407.12276},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2407.12276}
}The code and dataset in this repository are licensed under the MIT license.