{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/anomalydino-boosting-patch-based-few-shot","title":"AnomalyDINO: Boosting Patch-based Few-shot Anomaly Detection with DINOv2","arxiv_id":"2405.14529","date":"2024-05-23","proceeding":null,"authors":["Simon Damm","Mike Laszkiewicz","Johannes Lederer","Asja Fischer"],"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, 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.","url_abs":"https://arxiv.org/abs/2405.14529v2","url_pdf":"https://arxiv.org/pdf/2405.14529v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"anomalydino-boosting-patch-based-few-shot","repo_url":"https://github.com/dammsi/AnomalyDINO","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"anomaly-detection","task_name":"Anomaly Detection"},{"task_slug":"anomaly-segmentation","task_name":"Anomaly Segmentation"},{"task_slug":"few-shot-learning","task_name":"Few-Shot Learning"},{"task_slug":"meta-learning","task_name":"Meta-Learning"}],"methods":[{"method_slug":"set","method_name":"SET"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/anomaly-detection-on-mvtec-ad","task":"Anomaly Detection","dataset":"MVTec AD","model":"AnomalyDINO-S (full-shot)","rank_in_archive_order":28,"of":148,"metrics":{"Detection AUROC":"99.5","Segmentation AUPRO":"95","Segmentation AUROC":"98.2"},"uses_additional_data":false},{"leaderboard":"/sota/anomaly-detection-on-mvtec-ad","task":"Anomaly Detection","dataset":"MVTec AD","model":"AnomalyDINO-S (4-shot)","rank_in_archive_order":73,"of":148,"metrics":{"Detection AUROC":"97.7","Segmentation AUPRO":"93.4","Segmentation AUROC":"97.2"},"uses_additional_data":false},{"leaderboard":"/sota/anomaly-detection-on-mvtec-ad","task":"Anomaly Detection","dataset":"MVTec AD","model":"AnomalyDINO-S (2-shot)","rank_in_archive_order":79,"of":148,"metrics":{"Detection AUROC":"96.9","Segmentation AUPRO":"93.1","Segmentation AUROC":"97.0"},"uses_additional_data":false},{"leaderboard":"/sota/anomaly-detection-on-mvtec-ad","task":"Anomaly Detection","dataset":"MVTec AD","model":"AnomalyDINO-S (1-shot)","rank_in_archive_order":80,"of":148,"metrics":{"Detection AUROC":"96.6","Segmentation AUPRO":"92.7","Segmentation AUROC":"96.8"},"uses_additional_data":false},{"leaderboard":"/sota/anomaly-detection-on-visa","task":"Anomaly Detection","dataset":"VisA","model":"AnomalyDINO-S (full-shot)","rank_in_archive_order":14,"of":50,"metrics":{"Detection AUROC":"97.6","Segmentation AUPRO (until 30% FPR)":"96.1","Segmentation AUROC":"98.8"},"uses_additional_data":false},{"leaderboard":"/sota/anomaly-detection-on-visa","task":"Anomaly Detection","dataset":"VisA","model":"AnomalyDINO-S (4-shot)","rank_in_archive_order":26,"of":50,"metrics":{"Detection AUROC":"92.6","Segmentation AUPRO (until 30% FPR)":"94.1","Segmentation AUROC":"98.2"},"uses_additional_data":false},{"leaderboard":"/sota/anomaly-detection-on-visa","task":"Anomaly Detection","dataset":"VisA","model":"AnomalyDINO-S (2-shot)","rank_in_archive_order":29,"of":50,"metrics":{"Detection AUROC":"89.7","Segmentation AUPRO (until 30% FPR)":"93.4","Segmentation AUROC":"98"},"uses_additional_data":false},{"leaderboard":"/sota/anomaly-detection-on-visa","task":"Anomaly Detection","dataset":"VisA","model":"AnomalyDINO-S (1-shot)","rank_in_archive_order":32,"of":50,"metrics":{"Detection AUROC":"87.4","Segmentation AUPRO (until 30% FPR)":"92.5","Segmentation AUROC":"97.8"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2405.14529","atlas_url":"https://app.syntology.ai/?focus=2405.14529","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.14529"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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