Awesome Anomaly Detection Foundation Models

A curated list of papers & resources on anomaly detection foundation models using large language model, vision-language model, graph foundation model, time series foundation model, etc

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Anomaly Detection Foundation Models

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A collection of papers on anomaly detection (tabular data/time series/image/video/graph/text/log) with foundation models, e.g., large language model, large vision-language model, graph foundation model, time series foundation model, etc.

We will continue to update this list with the latest resources. If you find any missed resources (paper/code) or errors, please feel free to open an issue or make a pull request.

Framework of AnomalyGFM

Tutorial

📢 We are delighted to share that we have successfully hosted two tutorials titled

<ul><li> 👉 <a href="https://sites.google.com/view/iccv2025-tutorial-fm-driven-ad/home">Foundation Models in Visual Anomaly Detection: Advances, Challenges, and Applications</a> — at <a href="https://iccv.thecvf.com/virtual/2025/events/tutorial">ICCV 2025</a></li> </ul> <ul> <li>👉 <a href="https://sites.google.com/view/aaai26-tutorial-gad/home">Toward Foundation Models for Detecting Abnormal Activities on Graphs</a> — at <a href="https://sites.google.com/view/aaai26-tutorial-gad/home">AAAI 2026</a></li> </ul>

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[Jeong2023] WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation in CVPR, 2023. [paper][code]
[Chen2024] CLIP-AD: A Language-Guided Staged Dual-Path Model for Zero-shot Anomaly Detection in Arxiv, 2024. [paper][code]
[Gu2024] AnomalyGPT: Detecting Industrial Anomalies Using Large Vision-Language Models in AAAI, 2024. [paper][code]
[Zhou2024] AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detection in ICLR, 2024. [paper][code]
[Zhu2024] Toward Generalist Anomaly Detection via In-context Residual Learning with Few-shot Sample Prompts in CVPR, 2024. [paper][code]
[Li2024] PromptAD: Learning Prompts with only Normal Samples for Few-Shot Anomaly Detection in CVPR, 2024. [paper][code]
[Xu2024] Customizing Visual-Language Foundation Models for Multi-modal Anomaly Detection and Reasoning in Arxiv, 2024. [paper][code]
[Zhu2024] Do LLMs Understand Visual Anomalies? Uncovering LLM's Capabilities in Zero-shot Anomaly Detection in MM, 2024. [paper][code]
[Li2024] One-to-Normal: Anomaly Personalization for Few-shot Anomaly Detection in NeurIPS, 2024. [paper][code]
[Zuo2024] CLIP3D-AD: Extending CLIP for 3D Few-Shot Anomaly Detection with Multi-View Images Generation [paper][code]
[Zhu2025] Fine-grained Abnormality Prompt Learning for Zero-shot Anomaly Detection in ICCV, 2025. [paper][code]
[Tao2025] Kernel-Aware Graph Prompt Learning for Few-Shot Anomaly Detection in AAAI, 2025. [paper][code]
[Xu2025] Towards Zero-Shot Anomaly Detection and Reasoning with Multimodal Large Language Models in CVPR, 2025. [paper][code]
[Qu2025] Bayesian Prompt Flow Learning for Zero-Shot Anomaly Detection in CVPR, 2025. [paper][code]
[Ma2025] AA-CLIP: Enhancing Zero-Shot Anomaly Detection via Anomaly-Aware CLIP in CVPR, 2025. [paper][code]
[Zhang2025] Towards Training-free Anomaly Detection with Vision and Language Foundation Models in CVPR, 2025. [paper][code]
[Gu2025] UniVAD: A Training-free Unified Model for Few-shot Visual Anomaly Detection in CVPR, 2025. [paper][code]
[Yun2025] Language-Assisted Feature Transformation for Anomaly Detection in ICLR, 2025. [paper][code]
[Lv2025] One-for-All Few-Shot Anomaly Detection via Instance-Induced Prompt Learning in ICLR, 2025. [paper][code]
[Jiang2025] MMAD: A Comprehensive Benchmark for Multimodal Large Language Models in Industrial Anomaly Detection in ICLR, 2025. [paper][code]
[Guo2025] Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detection in CVPR, 2025. [paper][code]
[Li2025] One-for-More: Continual Diffusion Model for Anomaly DetectionOne-for-More: Continual Diffusion Model for Anomaly Detection in CVPR, 2025. [paper][code]
[Zeng2025] Towards Efficient and General-Purpose Few-Shot Misclassification Detection for Vision-Language Models in Arxiv, 2025. [paper][code]
[Luo2025] Exploring Intrinsic Normal Prototypes within a Single Image for Universal Anomaly Detection in CVPR, 2025. [paper][code]
[Sun2025] Anomaly Anything: Promptable Unseen Visual Anomaly Generation in CVPR, 2025. [paper][code]
[Sadikaj2025] MultiADS: Defect-aware Supervision for Multi-type Anomaly Detection and Segmentation in Zero-Shot Learning in Arxiv, 2025. [paper][code]
[Zhao2025] AnomalyHybrid: A Domain-agnostic Generative Framework for General Anomaly Detection in CVPRW, 2025. [paper][code]
[Kim2025] GenCLIP: Generalizing CLIP Prompts for Zero-shot Anomaly Detection in Arxiv, 2025. [paper][code]
[Chao2025] AnomalyR1: A GRPO-based End-to-end MLLM for Industrial Anomaly Detection in Arxiv, 2025. [paper][code]
[Hu2025] ReplayCAD: Generative Diffusion Replay for Continual Anomaly Detection in IJCAI, 2025. [paper][code]
[Gao2025] AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection in Arxiv, 2025. [paper][code]
[Yang2025] ViP-CLIP: Visual-Perception Prompting with Unified Alignment for Zero-Shot Anomaly Detection in Arxiv, 2025. [paper][code]
[Shiri2025] MadCLIP: Few-shot Medical Anomaly Detection with CLIP in MICCAI, 2025. [paper][code]
[Aqeel2025] A Contrastive Learning-Guided Confident Meta-learning for Zero-Shot Anomaly Detection in ICCV, 2025. [paper][code]
[Song2025] Normal and Abnormal Pathology Knowledge-Augmented Vision-Language Model for Anomaly Detection in Pathology Images in ICCV, 2025. [paper][code]
[Gu2025] AnomalyMoE: Towards a Language-free Generalist Model for Unified Visual Anomaly Detection in Arxiv, 2025. [paper][code]
[Chen2025] CoPS: Conditional Prompt Synthesis for Zero-Shot Anomaly Detection in Arxiv, 2025. [paper][code]
[Wang2025] Zero-Shot Anomaly Detection with Dual-Branch Prompt Selection in BMVC, 2025. [paper][code]
[Abdi2025] Zero-Shot Image Anomaly Detection Using Generative Foundation Models in ICCV, 2025. [paper][code]
[Fang2025] AF-CLIP: Zero-Shot Anomaly Detection via Anomaly-Focused CLIP Adaptation in ACM MM, 2025. [paper][code]
[Qu2025] DictAS: A Framework for Class-Generalizable Few-Shot Anomaly Segmentation via Dictionary Lookup in ICCV, 2025. [paper][code]
[He2025] RareCLIP: Rarity-aware Online Zero-shot Industrial Anomaly Detection ICCV, 2025. [paper][code]
[Wang2025] Normal-Abnormal Guided Generalist Anomaly Detection in NeurIPS, 2025. [paper][code]
[Shao2025] PromptMoE: Generalizable Zero-Shot Anomaly Detection via Visually-Guided Prompt Mixtures in AAAI, 2026. [paper][code]
[Xu2026] MRAD: Zero-Shot Anomaly Detection with Memory-Driven Retrieval in ICLR, 2026. [paper][[code]](https://gi