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

arXiv: https://arxiv.org/abs/2405.14529 · HTML: https://mlsanity.com/paper/2405.14529/

## Key facts

- Cited by 53 papers in the map (data through 14 Oct 2026).
- Builds on 38 of its 53 references that are in the map.
- First posted on arXiv 23 May 2024.

## Builds on (most cited first, 30 of 38)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://mlsanity.com/paper/2303.05499/) (2023)
- [Wide Residual Networks](https://mlsanity.com/paper/1605.07146/) (2016)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [Generalized Out-of-Distribution Detection: A Survey](https://mlsanity.com/paper/2110.11334/) (2021)
- [Towards Total Recall in Industrial Anomaly Detection](https://mlsanity.com/paper/2106.08265/) (2021)
- [PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization](https://mlsanity.com/paper/2011.08785/) (2020)
- [A Unifying Review of Deep and Shallow Anomaly Detection](https://mlsanity.com/paper/2009.11732/) (2020)
- [SPot-the-Difference Self-Supervised Pre-training for Anomaly Detection and Segmentation](https://mlsanity.com/paper/2207.14315/) (2022)
- [WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation](https://mlsanity.com/paper/2303.14814/) (2023)
- [Sub-Image Anomaly Detection with Deep Pyramid Correspondences](https://mlsanity.com/paper/2005.02357/) (2020)
- [Deep Semi-Supervised Anomaly Detection](https://mlsanity.com/paper/1906.02694/) (2019)
- [AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detection](https://mlsanity.com/paper/2310.18961/) (2023)
- [Multimodal Foundation Models: From Specialists to General-Purpose Assistants](https://mlsanity.com/paper/2309.10020/) (2023)
- [Diffusion Models for Medical Anomaly Detection](https://mlsanity.com/paper/2203.04306/) (2022)
- [Patch SVDD: Patch-level SVDD for Anomaly Detection and Segmentation](https://mlsanity.com/paper/2006.16067/) (2020)
- [APRIL-GAN: A Zero-/Few-Shot Anomaly Classification and Segmentation Method for CVPR 2023 VAND Workshop Challenge Tracks 1&2: 1st Place on Zero-shot AD and 4th Place on Few-shot AD](https://mlsanity.com/paper/2305.17382/) (2023)
- [Deep Learning for Medical Anomaly Detection -- A Survey](https://mlsanity.com/paper/2012.02364/) (2020)
- [Fully Convolutional Cross-Scale-Flows for Image-based Defect Detection](https://mlsanity.com/paper/2110.02855/) (2021)
- [FeatUp: A Model-Agnostic Framework for Features at Any Resolution](https://mlsanity.com/paper/2403.10516/) (2024)
- [Deep Nearest Neighbor Anomaly Detection](https://mlsanity.com/paper/2002.10445/) (2020)
- [Pushing the Limits of Fewshot Anomaly Detection in Industry Vision: Graphcore](https://mlsanity.com/paper/2301.12082/) (2023)

## Built on by (most cited first, 30 of 53)

- [BioCLIP 2: Emergent Properties from Scaling Hierarchical Contrastive Learning](https://mlsanity.com/paper/2505.23883/) (2025)
- [Large Language Models for Anomaly and Out-of-Distribution Detection: A Survey](https://mlsanity.com/paper/2409.01980/) (2024)
- [SegDINO: An Efficient Design for Medical and Natural Image Segmentation with DINO-V3](https://mlsanity.com/paper/2509.00833/) (2025)
- [MultiADS: Defect-aware Supervision for Multi-type Anomaly Detection and Segmentation in Zero-Shot Learning](https://mlsanity.com/paper/2504.06740/) (2025)
- [Normal-Abnormal Guided Generalist Anomaly Detection](https://mlsanity.com/paper/2510.00495/) (2025)
- [FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection](https://mlsanity.com/paper/2506.21398/) (2025)
- [SubspaceAD: Training-Free Few-Shot Anomaly Detection via Subspace Modeling](https://mlsanity.com/paper/2602.23013/) (2026)
- [CostFilter-AD: Enhancing Anomaly Detection through Matching Cost Filtering](https://mlsanity.com/paper/2505.01476/) (2025)
- [Anomaly Detection by Clustering DINO Embeddings using a Dirichlet Process Mixture](https://mlsanity.com/paper/2509.19997/) (2025)
- [Foundation Visual Encoders Are Secretly Few-Shot Anomaly Detectors](https://mlsanity.com/paper/2510.01934/) (2025)
- [Anonymization of Documents for Law Enforcement with Machine Learning](https://mlsanity.com/paper/2501.07334/) (2025)
- [View-Invariant Pixelwise Anomaly Detection in Multi-object Scenes with Adaptive View Synthesis](https://mlsanity.com/paper/2406.18012/) (2024)
- [KairosAD: A SAM-Based Model for Industrial Anomaly Detection on Embedded Devices](https://mlsanity.com/paper/2505.24334/) (2025)
- [DINO-AD: Unsupervised Anomaly Detection with Frozen DINO-V3 Features](https://mlsanity.com/paper/2602.03870/) (2026)
- [RAID: Retrieval-Augmented Anomaly Detection](https://mlsanity.com/paper/2602.19611/) (2026)
- [IAD-Unify: Task-Specific Interfaces for Industrial Anomaly Understanding, Segmentation, and Generation](https://mlsanity.com/paper/2604.12440/) (2026)
- [ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection](https://mlsanity.com/paper/2606.01300/) (2026)
- [Leveraging Intermediate Representations of Time Series Foundation Models for Anomaly Detection](https://mlsanity.com/paper/2509.12650/) (2025)
- [PatchEAD: Unifying Industrial Visual Prompting Frameworks for Patch-Exclusive Anomaly Detection](https://mlsanity.com/paper/2509.25856/) (2025)
- [SPG: Sparse-Projected Guides with Sparse Autoencoders for Zero-Shot Anomaly Detection](https://mlsanity.com/paper/2604.02871/) (2026)
- [SPARC: Subspace Position-Aware Robust Few-Shot Calibration for Distribution-Shifted Industrial Anomaly Detection](https://mlsanity.com/paper/2608.18585/) (2026)
- [NexViTAD: Few-shot Unsupervised Cross-Domain Defect Detection via Vision Foundation Models and Multi-Task Learning](https://mlsanity.com/paper/2507.07579/) (2025)
- [Anomalous Samples for Few-Shot Anomaly Detection](https://mlsanity.com/paper/2507.23712/) (2025)
- [Self-Navigated Residual Mamba for Universal Industrial Anomaly Detection](https://mlsanity.com/paper/2508.01591/) (2025)
- [Adversarially Robust Few-Shot Anomaly Detection with Vision Foundation Models](https://mlsanity.com/paper/2510.13643/) (2025)
- [Image-Intrinsic Priors for Integrated Circuit Defect Detection and Novel Class Discovery via Self-Supervised Learning](https://mlsanity.com/paper/2511.03120/) (2025)
- [UniADC: A Unified Framework for Anomaly Detection and Classification](https://mlsanity.com/paper/2511.06644/) (2025)
- [SSVP: Synergistic Semantic-Visual Prompting for Industrial Zero-Shot Anomaly Detection](https://mlsanity.com/paper/2601.09147/) (2026)
- [BAAF: Universal Transformation of One-Class Classifiers for Unsupervised Image Anomaly Detection](https://mlsanity.com/paper/2602.13091/) (2026)
- [Spatial Autoregressive Modeling of DINOv3 Embeddings for Unsupervised Anomaly Detection](https://mlsanity.com/paper/2603.02974/) (2026)

## Abstract (arXiv)

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.