AnomalyDINO: Boosting Patch-Based Few-Shot Anomaly Detection with DINOv2

Abstract

Recent advances in multimodal foundation models have set new standards in few-shot anomaly detection. This paper explores whether high-quality visual features alone are sufficient to rival existing state-of-the-art vision-language models. We affirm this by adapting DINOv2 for one-shot and few-shot anomaly detection with a focus on industrial applications. We show that this approach does not only rival existing techniques but can even outmatch them in many settings. Our proposed vision-only approach AnomalyDINO follows the well-established patch-level deep nearest neighbor paradigm and enables both image-level anomaly prediction and pixel-level anomaly segmentation. The approach is methodologically simple and training-free and thus does not require any additional data for fine-tuning or meta-learning. Despite its simplicity AnomalyDINO achieves state-of-the-art results in one- and few-shot anomaly detection (e.g. pushing the one-shot performance on MVTec-AD from an AUROC of 93.1% to 96.6%). The reduced overhead coupled with its outstanding few-shot performance makes AnomalyDINO a strong candidate for fast deployment e.g. in industrial contexts.