AnomalyDINO: Boosting Patch-based Few-shot Anomaly Detection with DINOv2
<|reference_start|>AnomalyDINO: Boosting Patch-based Few-shot Anomaly Detection with DINOv2: 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, is based on patch similarities 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.<|reference_end|>
@article{damm2024anomalydino:,
    title={AnomalyDINO: Boosting Patch-based Few-shot Anomaly Detection with DINOv2},
    author={Simon Damm, Mike Laszkiewicz, Johannes Lederer, Asja Fischer},
    journal={arXiv preprint arXiv:2405.14529},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.14529},
    primaryClass={cs.CV}
}