AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors

TL;DR

AnomalyVFM introduces a framework that transforms pretrained vision foundation models into effective zero-shot anomaly detectors by combining synthetic data generation and efficient adaptation techniques.

Contribution

It presents a novel approach that significantly improves zero-shot anomaly detection performance using vision foundation models, addressing dataset diversity and adaptation limitations.

Findings

Achieves 94.1% AUROC on average across 9 datasets

Outperforms previous methods by 3.3 percentage points

Utilizes a three-stage synthetic dataset and low-rank feature adapters

Abstract

Zero-shot anomaly detection aims to detect and localise abnormal regions in the image without access to any in-domain training images. While recent approaches leverage vision-language models (VLMs), such as CLIP, to transfer high-level concept knowledge, methods based on purely vision foundation models (VFMs), like DINOv2, have lagged behind in performance. We argue that this gap stems from two practical issues: (i) limited diversity in existing auxiliary anomaly detection datasets and (ii) overly shallow VFM adaptation strategies. To address both challenges, we propose AnomalyVFM, a general and effective framework that turns any pretrained VFM into a strong zero-shot anomaly detector. Our approach combines a robust three-stage synthetic dataset generation scheme with a parameter-efficient adaptation mechanism, utilising low-rank feature adapters and a confidence-weighted pixel loss. Together, these components enable modern VFMs to substantially outperform current state-of-the-art methods. More specifically, with RADIO as a backbone, AnomalyVFM achieves an average image-level AUROC of 94.1% across 9 diverse datasets, surpassing previous methods by significant 3.3 percentage points. Project Page: https://maticfuc.github.io/anomaly_vfm/