AnomalySD: Few-Shot Multi-Class Anomaly Detection with Stable Diffusion Model

• URL: http://arxiv.org/abs/2408.01960v1
• Date: Sun, 4 Aug 2024 08:33:44 GMT
• Title: AnomalySD: Few-Shot Multi-Class Anomaly Detection with Stable Diffusion Model
• Authors: Zhenyu Yan, Qingqing Fang, Wenxi Lv, Qinliang Su,
• Abstract summary: Anomaly detection is a critical task in industrial manufacturing, aiming to identify defective parts of products. Most industrial anomaly detection methods assume the availability of sufficient normal data for training. We propose a few-shot multi-class anomaly detection framework that adopts Stable Diffusion model.
• Score: 7.942354689705658
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Anomaly detection is a critical task in industrial manufacturing, aiming to identify defective parts of products. Most industrial anomaly detection methods assume the availability of sufficient normal data for training. This assumption may not hold true due to the cost of labeling or data privacy policies. Additionally, mainstream methods require training bespoke models for different objects, which incurs heavy costs and lacks flexibility in practice. To address these issues, we seek help from Stable Diffusion (SD) model due to its capability of zero/few-shot inpainting, which can be leveraged to inpaint anomalous regions as normal. In this paper, a few-shot multi-class anomaly detection framework that adopts Stable Diffusion model is proposed, named AnomalySD. To adapt SD to anomaly detection task, we design different hierarchical text descriptions and the foreground mask mechanism for fine-tuning SD. In the inference stage, to accurately mask anomalous regions for inpainting, we propose multi-scale mask strategy and prototype-guided mask strategy to handle diverse anomalous regions. Hierarchical text prompts are also utilized to guide the process of inpainting in the inference stage. The anomaly score is estimated based on inpainting result of all masks. Extensive experiments on the MVTec-AD and VisA datasets demonstrate the superiority of our approach. We achieved anomaly classification and segmentation results of 93.6%/94.8% AUROC on the MVTec-AD dataset and 86.1%/96.5% AUROC on the VisA dataset under multi-class and one-shot settings.

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