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arXiv · 2405.14529

AnomalyDINO: Boosting Patch-based Few-shot Anomaly Detection with DINOv2

Simon Damm·Mike Laszkiewicz·Johannes Lederer·Asja Fischer
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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. 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.

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Simon Damm, Mike Laszkiewicz, Johannes Lederer, Asja Fischer. 2025-03-13. AnomalyDINO: Boosting Patch-based Few-shot Anomaly Detection with DINOv2. https://arxiv.org/abs/2405.14529

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