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Awesome Industrial Anomaly Detection tool by M 3lab
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Awesome Industrial Anomaly Detection [](https://github.com/sindresorhus/awesome)
We discuss public datasets and related studies in detail. Welcome to read our paper and make comments.
Deep Industrial Image Anomaly Detection: A Survey (Machine Intelligence Research)
IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturing [TCYB 2024][code][δΈζ]
We will keep focusing on this field and updating relevant information.
Keywords: anomaly detection, anomaly segmentation, industrial image, defect detection
[Main Page] [Survey] [Benchmark] [Result]
π₯π₯π₯ Contributions to our repository are welcome. Feel free to categorize the papers and pull requests.
π₯π₯π₯ We have released AD-Copilot, an end-to-end trained MLLM for industrial anomaly detection. Most impressively, AD-Copilot surpasses humans on real industrial inspection tasks! Try it at [Code][Demo]
π₯π₯π₯ How well are current MLLMs performing as industrial quality inspectors? Which MLLM performs best in industrial anomaly detection? Please refer to our recent research. [ICLR 2025][Github]
π₯π₯π₯ We compare different types of anomaly synthesis methods in detail. Welcome to make comments.
ASBench: Image Anomalies Synthesis Benchmark for Anomaly Detection [paper]
A Survey on Industrial Anomalies Synthesis [paper][github]
π₯π₯π₯ 3D Anomaly Detection: A Survey [paper] [github]
Table of Contents
- Awesome Industrial Anomaly Detection
- SOTA methods with code
- Recommended Benchmarks
- Recent research
- Paper Tree (Classification of representative methods)
- Timeline
- Paper list for industrial image anomaly detection
- Related Survey, Benchmark, and Framework
- 2 Unsupervised AD
- 3 Other Research Direction
- 4 Dataset
SOTA methods with code
Recommended Benchmarks
| Title | Venue | Date | Code | topic |
|---|---|---|---|---|
 Anomalib: A Deep Learning Library for Anomaly Detection |
ICIP | 2022 | Github | Benchmark |
 IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturing |
TCYB | 2024 | Github | Benchmark |
 ADer: A Comprehensive Benchmark for Multi-class Visual Anomaly Detection |
arxiv | 2024 | Github | Benchmark |
 MMAD: The First-Ever Comprehensive Benchmark for Multimodal Large Language Models in Industrial Anomaly Detection |
ICLR | 2024 | Github | Benchmark |
- Anomaly Detection on MVTec AD [paper with code]
- Anomaly Detection on VisA [paper with code]
- Anomaly Detection on MVTec LOCO AD [paper with code]
- Anomaly Detection on MVTec 3D-AD [paper with code]
- Anomaly Detection Datasets and Benchmarks [paper with code]
Recent research
CVPR 2026
- Towards an Incremental Unified Multimodal Anomaly Detection: Augmenting Multimodal [CVPR 2026]
- InvAD: Inversion-based Reconstruction-Free Anomaly Detection with Diffusion Models [CVPR 2026][code]
- SubspaceAD: Training-Free Few-Shot Anomaly Detection via Subspace Modeling [CVPR 2026][code]
- VisualAD: Language-Free Zero-Shot Anomaly Detection via Vision Transformer [CVPR 2026][code]
- Bidirectional Multimodal Prompt Learning with Scale-Aware Training for Few-Shot Multi-Class Anomaly Detection [CVPR 2026][code]
- MAGIC: Few-Shot Mask-Guided Anomaly Inpainting with Prompt Perturbation, Spatially Adaptive Guidance, and Context Awareness [CVPR 2026 Findings][code]
- DLVP-CLIP: Enhancing Fine-Grained Zero-Shot Anomaly Detection via Dynamic Local Visual Prompting [CVPR 2026]
- Back to Point: Exploring Point-Language Models for Zero-Shot 3D Anomaly Detection [CVPR 2026][code]
- AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors [CVPR 2026][code]
- MoECLIP: Patch-Specialized Experts for Zero-shot Anomaly Detection [CVPR 2026][code]
- AG-VAS: Anchor-Guided Zero-Shot Visual Anomaly Segmentation with Large Multimodal Models [CVPR 2026][code]
- Wavelet-Driven 3D Anomaly Detection under Pose-Agnostic and Sparse-View [CVPR 2026]
- PDD: Manifold-Prior Diverse Distillation for Medical Anomaly Detection [CVPR 2026][code]
- CoPS: Conditional Prompt Synthesis for Zero-Shot Anomaly Detection [CVPR 2026 Findings][code]
- One-to-More: High-Fidelity Training-Free Anomaly Generation with Attention Control [CVPR 2026][code]
- FB-CLIP: Fine-Grained Zero-Shot Anomaly Detection with Foreground-Background Disentanglement [CVPR 2026][code]
- FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection [CVPR 2026]
- RAID: Retrieval-Augmented Anomaly Detection [CVPR 2026][code]
- Complementary Prototype Mapping for Efficient Multimodal Anomaly Detection [CVPR 2026]
- GPFlow: Gaussian Prototype Probability Flow for Unsupervised Multi-Modal Anomaly Detection [CVPR 2026]
- Hierarchical Point-Patch Fusion with Adaptive Patch Codebook for 3D Shape Anomaly Detection [CVPR 2026][code]
- GS-CLIP: Zero-shot 3D Anomaly Detection by Geometry-Aware Prompt and Synergistic View Representation Learning [CVPR 2026][code]
- Geometry-Aligned and Anomaly-Aware Reconstruction for 3D Anomaly Detection [CVPR 2026]
- Reasoning-Driven Anomaly Detection and Localization with Image-Level Supervision [CVPR 2026][code]
- MMR-AD: A Large-Scale Multimodal Dataset for Benchmarking General Anomaly Detection with Multimodal Large Language Models [CVPR 2026][code]
- ADSeeker: A Knowledge-Grounded Reasoning Framework for Industry Anomaly Detection and Reasoning [CVPR 2026]
- Multi-Prototype Compactness and Boundary-Aware Synthesis for Unsupervised Anomaly Detection [CVPR 2026]
- Dual-Prototype-Guided Multi-task Learning for Unsupervised Anomaly Detection and Classification [CVPR 2026]
- Defect Cue-Preserved Structural Feature Refinement for Few-Shot Anomaly Detection [CVPR 2026]
- UniMMAD: Unified Multi-Modal and Multi-Class Anomaly Detection via MoE-Driven Feature Decompression [CVPR 2026][code]
- Omni-AD: A Large-scale and Versatile Benchmark for Industrial Anomaly Detection [CVPR 2026]
- From Attraction to Equilibrium: Physics-Inspired Semantic Gravitons for Zero-Shot Anomaly Detection [CVPR 2026]
- Anomaly as Non-Conformity via Training-Free Graph Laplacian Energy Minimization [CVPR 2026]
- A Semantically Disentangled Unified Model for Multi-category 3D Anomaly Detection [CVPR 2026][code]
- Hyperbolic Defect Feature Synthesis for Few-Shot Defect Classification [CVPR 2026]
- UniSpector: Towards Universal Open-set Defect Recognition via Spectral-Contrastive Visual Prompting [CVPR 2026]
- Towards Open-Vocabulary Industrial Defect Understanding with a Large-Scale Multimodal Dataset [CVPR 2026][data]
ICLR 2026
- Foundation Visual Encoders Are Secretly Few-Shot Anomaly Detectors [ICLR 2026][code]
- MRAD: Zero-Shot Anomaly Detection with Memory-Driven Retrieval [ICLR 2026]
- PIRN: Prototypical-based Intra-modal Reconstruction with Normality Communication for Multi-modal Anomaly Detection [ICLR 2026]
- Dual Distillation for Few-Shot Anomaly Detection [ICLR 2026]
- Judo: A Juxtaposed Domain-oriented Multimodal Reasoner for Industrial Anomaly QA [ICLR 2026]
AAAI 2026
- Towards High-Resolution 3D Anomaly Detection: A Scalable Dataset and Real-Time Framework for Subtle Industrial Defects [AAAI 2026 oral][code]
- AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection [AAAI 2026][code]
- AnomalyMoE: Towards a Language-free Generalist Model for Unified Visual Anomaly Detection [AAAI 2026][code]
- AnoStyler: Text-Driven Localized Anomaly Generation via Lightweight Style Transfer [AAAI 2026][code]
- Anomagic: Crossmodal Prompt-driven Zero-shot Anomaly Generation [AAAI 2026][code]
- Commonality in Few: Few-Shot Multimodal Anomaly Detection via Hypergraph-Enhanced Memory [AAAI 2026][code]
- IAD-R1: Reinforcing Consistent Reasoning in Industrial Anomaly Detection [ICCV 2025][code]
- CASL: Curvature-Augmented Self-supervised Learning for 3D Anomaly Detection [AAAI 2026][code]
- MaskAD: Parallel Masked Autoencoder for Multi-class Unsupervised Anomaly Detection [AAAI 2026][code]
- CHIMERA:Controllable High-quality Image-Mask Extraction for Reliable Diffusion-Based Anomaly Synthesis [AAAI 2026][code]
- PromptMoE: Generalizable Zero-Shot Anomaly Detection via Visually-Guided Prompt Mixtures [AAAI 2026][code]
- Unsupervised Multi-View Visual Anomaly Detection via Progressive Homography-Guided Alignment [AAAI 2026]
- AnomalyPainter: Vision-Language-Diffusion Synergy for Zero-Shot Realistic and Diverse Industrial Anomaly Synthesis [AAAI 2026]
- Exploring High-order-aware Prompt Learning for Zero-shot Anomaly Detection [AAAI 2026]
- RcAE: Recursive Reconstruction Framework for Unsupervised Industrial Anomaly Detection [AAAI 2026]
- CADiff: Context-Aware Diffusion for Controllable Anomaly Generation in Anomaly Detection [AAAI 2026]
- Quality-Aware Language-Conditioned Local Auto-Regressive Anomaly Synthesis and Detection [AAAI 2026][code]
- MAU-GPT: Enhancing Multi-type Industrial Anomaly Understanding via Anomaly-aware and Generalist Experts Adaptation [AAAI 2026]
- SCoNE: Spherical Consistent Neighborhoods Ensemble for Effective and Efficient Multi-View Anomaly Detection [AAAI 2026]
- Commonality in Few: Few-Shot Multimodal Anomaly Detection via Hypergraph-Enhanced Memory [AAAI 2026][code]
- RPE-PAD: Relative Pose Estimation for Pose-agnostic Anomaly Detection [AAAI 2026]
- AD-FM: Multimodal LLMs for Anomaly Detection via Multi-Stage Reasoning and Fine-Grained Reward Optimization [AAAI 2026]
- FDP: A Frequency-Decomposition Preprocessing Pipeline for Unsupervised Anomaly Detection in Brain MRI [AAAI 2026][code]
NeurIPS 2025
- FAST: Foreground-aware Diffusion with Accelerated Sampling Trajectory for Segmentation-oriented Anomaly Synthesis [NeurIPS 2025][code]
- Normal-Abnormal Guided Generalist Anomaly Detection [NeurIPS 2025][code]
- Registration is a Powerful Rotation-Invariance Learner for 3D Anomaly Detection [NeurIPS 2025]
- ADPretrain: Advancing Industrial Anomaly Detection via Anomaly Representation Pretraining [NeurIPS 2025][code]
KDD 2025
- Self-Tuning Self-Supervised Image Anomaly Detection [KDD 2025] [code]
- Logical Anomaly Detection with Text-based Logic via Component-Aware Contrastive Language-Image Training [KDD 25]
ICCV 2025
- SeaS: Few-shot Industrial Anomaly Image Generation with Separation and Sharing Fine-tuning [ICCV 2025][code]
- MultiADS: Defect-aware Supervision for Multi-type Anomaly Detection and Segmentation in Zero-Shot Learning [ICCV 2025][code]
- Towards Real Unsupervised Anomaly Detection Via Confident Meta-Learning [ICCV 2025]
- DictAS: A Framework for Class-Generalizable Few-Shot Anomaly Segmentation via Dictionary Lookup [ICCV 2025][code]
- SALAD -- Semantics-Aware Logical Anomaly Detection [ICCV 2025][code]
- Kaputt: A Large-Scale Dataset for Visual Defect Detection [ICCV 2025][data]
- G2SF: Geometry-Guided Score Fusion for Multimodal Industrial Anomaly Detection[ICCV 2025][code]
- FE-CLIP: Frequency Enhanced CLIP Model for Zero-Shot Anomaly Detection and Segmentation [ICCV 2025]
- DecAD: Decoupling Anomalies in Latent Space for Multi-Class Unsupervised Anomaly Detection [ICCV 2025]
- Triad: Empowering LMM-based Anomaly Detection with Vision Expert-guided Visual Tokenizer and Manufacturing Process [ICCV 2025][code]
- Fine-grained Abnormality Prompt Learning for Zero-shot Anomaly Detection [ICCV 2025][code]
- Anomaly Detection of Integrated Circuits Package Substrates Using the Large Vision Model SAIC: Dataset Construction, Methodology, and Application [ICCV 2025][data]
- Debiasing Trace Guidance: Top-down Trace Distillation and Bottom-up Velocity Alignment for Unsupervised Anomaly Detection [ICCV 2025]
- FIND: Few-Shot Anomaly Inspection with Normal-Only Multi-Modal Data [ICCV 2025]
- Bridging 3D Anomaly Localization and Repair via High-Quality Continuous Geometric Representation [ICCV 2025][code]
- SiM3D: Single-instance Multiview Multimodal and Multisetup 3D Anomaly Detection Benchmark [ICCV 2025][data]
- Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts [ICCV 2025][data]
- ReMP-AD: Retrieval-enhanced Multi-modal Prompt Fusion for Few-Shot Industrial Visual Anomaly Detection [ICCV 2025][code]
- RareCLIP: Rarity-aware Online Zero-shot Industrial Anomaly Detection [ICCV 2025][code]
- Wave-MambaAD: Wavelet-driven State Space Model for Multi-class Unsupervised Anomaly Detection [ICCV 2025]
- Salvaging the Overlooked: Leveraging Class-Aware Contrastive Learning for Multi-Class Anomaly Detection [ICCV 2025][code]
- Training-Free Industrial Defect Generation with Diffusion Models [ICCV 2025][code]
ICML 2025
- CostFilter-AD: Enhancing Anomaly Detection through Matching Cost Filtering [ICML2025][code]
- OmiAD: One-Step Adaptive Masked Diffusion Model for Multi-class Anomaly Detection via Adversarial Distillation [ICML2025]
- Demeaned Sparse: Efficient Anomaly Detection by Residual Estimate [ICML2025]
CVPR 2025
- Anomaly Anything: Promptable Unseen Visual Anomaly Generation [CVPR 2025][code]
- Real-IAD D3: A Real-World 2D/Pseudo-3D/3D Dataset for Industrial Anomaly Detection [CVPR 2025]
- Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection [CVPR 2025][code]
- One-for-More: Continual Diffusion Model for Anomaly Detection [CVPR 2025][code]
- Exploring Intrinsic Normal Prototypes within a Single Image for Universal Anomaly Detection [CVPR 2025][code]
- UniVAD: A Training-free Unified Model for Few-shot Visual Anomaly Detection [CVPR 2025][code]
- Towards Visual Discrimination and Reasoning of Real-World Physical Dynamics: Physics-Grounded Anomaly Detection [CVPR 2025][code]
- Odd-One-Out: Anomaly Detection by Comparing with Neighbors [CVPR 2025][code]
- UniNet: A Contrastive Learning-guided Unified Framework with Feature Selection for Anomaly Detection [CVPR 2025][code]
- Towards Zero-Shot Anomaly Detection and Reasoning with Multimodal Large Language Models [CVPR 2025][code]
- MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects [CVPR 2025][data]
- AA-CLIP: Enhancing Zero-shot Anomaly Detection via Anomaly-Aware CLIP [CVPR 2025][code]
- AnomalyNCD: Towards Novel Anomaly Class Discovery in Industrial Scenarios [CVPR 2025][code]
- Towards Training-free Anomaly Detection with Vision and Language Foundation Models [CVPR 2025][code]
- TailedCore: Few-Shot Sampling for Unsupervised Long-Tail Noisy Anomaly Detection [CVPR 2025][code]
- DualAnoDiff: Dual-Interrelated Diffusion Model for Few-Shot Anomaly Image Generation [CVPR 2025][code]
- PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly Detection [CVPR 2025]
- Multi-Sensor Object Anomaly Detection: Unifying Appearance, Geometry, and Internal Properties [CVPR 2025][code]
- Bayesian Prompt Flow Learning for Zero-Shot Anomaly Detection [CVPR 2025][code coming soon]
- DefectFill: Realistic Defect Generation with Inpainting Diffusion Model for Visual Inspection [CVPR 2025]
- Correcting Deviations from Normality: A Reformulated Diffusion Model for Multi-Class Unsupervised Anomaly Detection [CVPR 2025][code]
- Dinomaly: The Less is More Philosophy in Multi-Class Unsupervised Anomaly Detection [CVPR 2025][code]
- A Unified Latent SchrΓΆdinger Bridge Diffusion Model for Unsupervised Anomaly Detection and Localization[CVPR 2025][code]
- DFM: Differentiable Feature Matching for Anomaly Detection[CVPR 2025]
- Correcting Deviations from Normality: A Reformulated Diffusion Model for Multi-Class Unsupervised Anomaly Detection[CVPR 2025][code]
- Beyond Single-Modal Boundary: Cross-Modal Anomaly Detection through Visual Prototype and Harmonization[CVPR 2025][code]
- PatchGuard: Adversarially Robust Anomaly Detection and Localization through Vision Transformers and Pseudo Anomalies[CVPR 2025][code]
- Wavelet and Prototype Augmented Query-based Transformer for Pixel-level Surface Defect Detection[CVPR 2025][code]
- VAND 3.0: Visual Anomaly and Novelty Detection - 3rd Edition [CVPR 2025W]
- Feature Attenuation of Defective Representation Can Resolve Incomplete Masking on Anomaly Detection [CVPR 2025 VAND 3.0 Workshop]
- RoBiS: Robust Binary Segmentation for High-Resolution Industrial Images [CVPR 2025 VAND 3.0 Workshop][code]
- When Textures Deceive: Weakly Supervised Industrial Anomaly Detection with Adapted-Loss (AL-CycleGAN) [CVPR 2025 VAND Workshop][code][data / MCBT]
- AnomalyHybrid: A Domain-agnostic Generative Framework for General Anomaly Detection [CVPR 2025 SyntaGen Workshop]
Paper Tree (Classification of representative methods)
Timeline
Paper list for industrial image anomaly detection
Related Survey, Benchmark, and Framework
- A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure [2015]
- Visual-based defect detection and classification approaches for industrial applications: a survey [2020]
- A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challenges [TMLR 2022]
- Deep Learning for Unsupervised Anomaly Localization in Industrial Images: A Survey [TIM 2022]
- A Survey on Unsupervised Industrial Anomaly Detection Algorithms [2022]
- A Survey of Methods for Automated Quality Control Based on Images [IJCV 2023][github page]
- Benchmarking Unsupervised Anomaly Detection and Localization [2022]
- IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturing [TCYB 2024][code][δΈζ]
- A Deep Learning-based Software for Manufacturing Defect Inspection [TII 2017][code]
- Anomalib: A Deep Learning Library for Anomaly Detection [ICIP 2022][code]
- Ph.D. thesis of Paul Bergmann(The first author of MVTec AD series) [2022]
- CVPR 2023 Tutorial on "Recent Advances in Anomaly Detection" [CVPR Workshop 2023][video]
- A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect [2024]
- AUPIMO: Redefining Visual Anomaly Detection Benchmarks with High Speed and Low Tolerance [2024]
- Explainable Anomaly Detection in Images and Videos: A Survey [2024][repo]
- RAD: A Comprehensive Dataset for Benchmarking the Robustness of Image Anomaly Detection [CASE 2024][github page]
- Generalized Out-of-Distribution Detection and Beyond in Vision Language Model Era: A Survey [2024][github page]
- Large Language Models for Anomaly and Out-of-Distribution Detection: A Survey [2024][github page]
- A Survey on RGB, 3D, and Multimodal Approaches for Unsupervised Industrial Anomaly Detection [2024][github page]
- OpenOOD: Benchmarking Generalized Out-of-Distribution Detection [NeurIPS2022v1][2024v1.5][github page]
- Exploring Plain ViT Reconstruction for Multi-class Unsupervised Anomaly Detection [CVIU 2025][code]
- A Survey on Foundation-Model-Based Industrial Defect Detection [2025]
- Foundation Models for Anomaly Detection: Vision and Challenges [2025]
- Beyond Academic Benchmarks: Critical Analysis and Best Practices for Visual Industrial Anomaly Detection [2025][code]
- A Comprehensive Survey for Real-World Industrial Defect Detection: Challenges, Approaches, and Prospects [2025]
- Towards High-Resolution Industrial Image Anomaly Detection [2025][code]
- ASBench: Image Anomalies Synthesis Benchmark for Anomaly Detection [TAI 2026][code]
2 Unsupervised AD
2.1 Feature-Embedding-based Methods
2.1.1 Teacher-Student
- Contextual Affinity Distillation for Image Anomaly Detection [WACV 2024]
- Revisiting Reverse Distillation for Anomaly Detection [CVPR 2023] [code]
- Uninformed students: Student-teacher anomaly detection with discriminative latent embeddings [CVPR 2020]
- Multiresolution knowledge distillation for anomaly detection [CVPR 2021]
- Glancing at the Patch: Anomaly Localization With Global and Local Feature Comparison [CVPR 2021]
- Reconstruction Student with Attention for Student-Teacher Pyramid Matching [2021]
- Student-Teacher Feature Pyramid Matching for Anomaly Detection [2021][code]
- PFM and PEFM for Image Anomaly Detection and Segmentation [CASE 2022] [TII 2022][code]
- Reconstructed Student-Teacher and Discriminative Networks for Anomaly Detection [2022]
- Anomaly Detection via Reverse Distillation from One-Class Embedding [CVPR 2022][code]
- Asymmetric Student-Teacher Networks for Industrial Anomaly Detection [WACV 2022][code]
- Informative knowledge distillation for image anomaly segmentation [2022][code]
- Learning deep feature correspondence for unsupervised anomaly detection and segmentation[PR 2022][code]
- Remembering Normality: Memory-guided Knowledge Distillation for Unsupervised Anomaly Detection [ICCV 2023]
- A Discrepancy Aware Framework for Robust Anomaly Detection [2023][code]
- Enhanced multi-scale features mutual mapping fusion based on reverse knowledge distillation for industrial anomaly detection and localization [TBD 2024]
- AEKD: Unsupervised auto-encoder knowledge distillation for industrial anomaly detection [JMS 2024]
- Masked feature regeneration based asymmetric studentβteacher network for anomaly detection [Multimedia Tools and Applications 2024]
- Feature-Constrained and Attention-Conditioned Distillation Learning for Visual Anomaly Detection [ICASSP 2024]
- MiniMaxAD: A Lightweight Autoencoder for Feature-Rich Anomaly Detection [2024]
- Enhanced Fabric Defect Detection with Feature Contrast Interference Suppression [TIM 2025]
- Anomaly Detection and Localization via Reverse Distillation with Latent Anomaly Suppression [TCSVT 2025]
2.1.2 One-Class Classification (OCC)
- Patch svdd: Patch-level svdd for anomaly detection and segmentation [ACCV 2020]
- Anomaly detection using improved deep SVDD model with data structure preservation [2021]
- A Semantic-Enhanced Method Based On Deep SVDD for Pixel-Wise Anomaly Detection [2021]
- MOCCA: Multilayer One-Class Classification for Anomaly Detection [2021]
- Defect Detection of Metal Nuts Applying Convolutional Neural Networks [2021]
- Panda: Adapting pretrained features for anomaly detection and segmentation [2021]
- Mean-shifted contrastive loss for anomaly detection [2021]
- Learning and Evaluating Representations for Deep One-Class Classification [2020]
- Self-supervised learning for anomaly detection with dynamic local augmentation [2021]
- Contrastive Predictive Coding for Anomaly Detection [2021]
- Cutpaste: Self-supervised learning for anomaly detection and localization [ICCV 2021][unofficial code]
- Consistent estimation of the max-flow problem: Towards unsupervised image segmentation [2020]
- MemSeg: A semi-supervised method for image surface defect detection using differences and commonalities [2022][unofficial code]
- SimpleNet: A Simple Network for Image Anomaly Detection and Localization [CVPR 2023][code]
- End-to-End Augmentation Hyperparameter Tuning for Self-Supervised Anomaly Detection [2023]
- Anomaly Detection under Distribution Shift [ICCV 2023][code]
- Learning Transferable Representations for Image Anomaly Localization Using Dense Pretraining [WACV 2024][code]
- GeneralAD: Anomaly Detection Across Domains by Attending to Distorted Features [ECCV 2024][code]
- A Unified Anomaly Synthesis Strategy with Gradient Ascent for Industrial Anomaly Detection and Localization [ECCV 2024][code]
- Dual-Modeling Decouple Distillation for Unsupervised Anomaly Detection [ACM MM 2024]
- SuperSimpleNet: Unifying Unsupervised and Supervised Learning for Fast and Reliable Surface Defect Detection [ICPR 2024][JIMS 2025][code]
- Progressive Boundary Guided Anomaly Synthesis for Industrial Anomaly Detection [TCSVT 2024][code]
2.1.3 Distribution-Map
- Anomaly Detection in Nanofibrous Materials by CNN-Based Self-Similarity [Sensors 2018]
- A Multi-Scale A Contrario method for Unsupervised Image Anomaly Detection [2021]
- Modeling the distribution of normal data in pre-trained deep features for anomaly detection [2021]
- Transfer Learning Gaussian Anomaly Detection by Fine-Tuning Representations [2021]
- PEDENet: Image anomaly localization via patch embedding and density estimation [2022]
- Unsupervised image anomaly detection and segmentation based on pre-trained feature mapping [2022]
- Position Encoding Enhanced Feature Mapping for Image Anomaly Detection [2022][code]
- Focus your distribution: Coarse-to-fine non-contrastive learning for anomaly detection and localization [ICME 2022]
- Anomaly Detection of Defect using Energy of Point Pattern Features within Random Finite Set Framework [2021][code]
- Fastflow: Unsupervised anomaly detection and localization via 2d normalizing flows [2021][unofficial code]
- Same same but differnet: Semi-supervised defect detection with normalizing flows [WACV 2021][code]
- Fully convolutional cross-scale-flows for image-based defect detection [WACV 2022][code]
- Cflow-ad: Real-time unsupervised anomaly detection with localization via conditional normalizing flows [WACV 2022][code]
- CAINNFlow: Convolutional block Attention modules and Invertible Neural Networks Flow for anomaly detection and localization tasks [2022]
- AltUB: Alternating Training Method to Update Base Distribution of Normalizing Flow for Anomaly Detection [2022]
- Collaborative Discrepancy Optimization for Reliable Image Anomaly Localization [TII 2023][code]
- PyramidFlow: High-Resolution Defect Contrastive Localization using Pyramid Normalizing Flow [CVPR 2023][code]
- Attention Modules Improve Image-Level Anomaly Detection for Industrial Inspection: A DifferNet Case Study [WACV 2024]
- Fascinating Supervisory Signals and Where to Find Them: Deep Anomaly Detection with Scale Learning [ICML 2023]
- FRAnomaly: flow-based rapid anomaly detection from images [Applied Intelligence 2024]
- Image alignment-based patch distribution framework for anomaly detection [ICCVDM 2024]
- MSFlow: Multi-Scale Flow-based Framework for Unsupervised Anomaly Detection [2024][code]
- Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection [CVPR 2025][code]
- Multi-Prototype Compactness and Boundary-Aware Synthesis for Unsupervised Anomaly Detection [CVPR 2026]
2.1.4 Memory Bank
- ReConPatch: Contrastive Patch Representation Learning for Industrial Anomaly Detection [WACV 2024]
- Sub-image anomaly detection with deep pyramid correspondences [2020]
- Semi-orthogonal embedding for efficient unsupervised anomaly segmentation [2021]
- Anomaly Detection Via Self-Organizing Map [2021]
- PaDiM: A Patch Distribution Modeling Framework for Anomaly Detection and Localization [ICPR 2021][unofficial code]
- Industrial Image Anomaly Localization Based on Gaussian Clustering of Pretrained Feature [2021]
- Towards total recall in industrial anomaly detection[CVPR 2022][code]
- CFA: Coupled-Hypersphere-Based Feature Adaptation for Target-Oriented Anomaly Localization[2022][code]
- FAPM: Fast Adaptive Patch Memory for Real-time Industrial Anomaly Detection[2022]
- N-pad: Neighboring Pixel-based Industrial Anomaly Detection [2022]
- Multi-scale patch-based representation learning for image anomaly detection and segmentation [2022]
- SPot-the-Difference Self-supervised Pre-training for Anomaly Detection and Segmentation [ECCV 2022]
- Diversity-Measurable Anomaly Detection [CVPR 2023]
- Self-supervised Context Learning for Visual Inspection of Industrial Defects [2023][code]
- SelFormaly: Towards Task-Agnostic Unified Anomaly Detection[2023]
- REB: Reducing Biases in Representation for Industrial Anomaly Detection [2023][code]
- PNI : Industrial Anomaly Detection using Position and Neighborhood Information [ICCV 2023][code]
- Inter-Realization Channels: Unsupervised Anomaly Detection Beyond One-Class Classification [ICCV 2023][code]
- Grid-Based Continuous Normal Representation for Anomaly Detection [2024][code]
- PointCore: Efficient Unsupervised Point Cloud Anomaly Detector Using Local-Global Features [2024]
- DMAD: Dual Memory Bank for Real-World Anomaly Detection [2024]
- A Reconstruction-Based Feature Adaptation for Anomaly Detection with Self-Supervised Multi-Scale Aggregation [ICASSP 2024]
- AnomalousPatchCore: Exploring the Use of Anomalous Samples in Industrial Anomaly Detection [ECCVW 2024]
- VQ-Flow: Taming Normalizing Flows for Multi-Class Anomaly Detection via Hierarchical Vector Quantization [2024][code]
- FOCT: Few-shot Industrial Anomaly Detection with Foreground-aware Online Conditional Transport [ACM MM 2024]
- Unsupervised, Online and On-The-Fly Anomaly Detection For Non-Stationary Image Distributions [ECCV 2024][[code]]
- Target before Shooting: Accurate Anomaly Detection and Localization under One Millisecond via Cascade Patch Retrieval [TIP 2024][code]
- Tailored Transformation Invariance for Industrial Anomaly Detection [2025][code]
- EAGLE: Expert-Augmented Attention Guidance for Tuning-Free Industrial Anomaly Detection in Multimodal Large Language Models [2026][code]
- RAID: Retrieval-Augmented Anomaly Detection [CVPR 2026][code]
- Anomaly as Non-Conformity via Training-Free Graph Laplacian Energy Minimization [CVPR 2026]
2.1.5 Vison Language AD
- Random Word Data Augmentation with CLIP for Zero-Shot Anomaly Detection [BMVC 2023]
- AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detection [ICLR 2024][code]
- WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation [CVPR 2023]
- ClipSAM: CLIP and SAM Collaboration for Zero-Shot Anomaly Segmentation [2023]
- CLIP-AD: A Language-Guided Staged Dual-Path Model for Zero-shot Anomaly Detection [2023]
- AnoVL: Adapting Vision-Language Models for Unified Zero-shot Anomaly Localization [2023][code]
- AnomalyGPT: Detecting Industrial Anomalies using Large Vision-Language Models [AAAI 2024][code][project page]
- Anomaly Detection by Adapting a pre-trained Vision Language Model [2024]
- Customizing Visual-Language Foundation Models for Multi-modal Anomaly Detection and Reasoning [2024][code]
- PromptAD: Learning Prompts with only Normal Samples for Few-Shot Anomaly Detection [CVPR 2024][code]
- Do LLMs Understand Visual Anomalies? Uncovering LLM Capabilities in Zero-shot Anomaly Detection [2024]
- FiLo: Zero-Shot Anomaly Detection by Fine-Grained Description and High-Quality Localization [2024]
- Dual-Image Enhanced CLIP for Zero-Shot Anomaly Detection [2024]
- AnoPLe: Few-Shot Anomaly Detection via Bi-directional Prompt Learning with Only Normal Samples [2024][code]
- GlocalCLIP: Object-agnostic Global-Local Prompt Learning for Zero-shot Anomaly Detection [2024]
- UniVAD: A Training-free Unified Model for Few-shot Visual Anomaly Detection [2024][code]
- One-to-Normal: Anomaly Personalization for Few-shot Anomaly Detection [NeurIPS 2024]
- SEM-CLIP: Precise Few-Shot Learning for Nanoscale Defect Detection in Scanning Electron Microscope Image [2025]
- PA-CLIP: Enhancing Zero-Shot Anomaly Detection through Pseudo-Anomaly Awareness [2025]
- Language-Assisted Feature Transformation for Anomaly Detection [ICLR 2025]
- Detect, Classify, Act: Categorizing Industrial Anomalies with Multi-Modal Large Language Models [2025]
- AnomalyR1: A GRPO-based End-to-end MLLM for Industrial Anomaly Detection [2025]
- MultiADS: Defect-aware Supervision for Multi-type Anomaly Detection and Segmentation in Zero-Shot Learning [ICCV 2025][code]
- OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning[2025]
- EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware [2025][code]
- CoPS: Conditional Prompt Synthesis for Zero-Shot Anomaly Detection [2025][code]
- IAD-GPT: Advancing Visual Knowledge in Multimodal Large Language Model for Industrial Anomaly Detection [2025][code]
- VLMDiff: Leveraging Vision-Language Models for Multi-Class Anomaly Detection with Diffusion [2025][code]
- Triad: Empowering LMM-based Anomaly Detection with Vision Expert-guided Visual Tokenizer and Manufacturing Process [ICCV 2025][code]
- EAGLE: Expert-Augmented Attention Guidance for Tuning-Free Industrial Anomaly Detection in Multimodal Large Language Models [2026][code]
2.2 Reconstruction-Based Methods
2.2.1 Autoencoder (AE)
- Improving unsupervised defect segmentation by applying structural similarity to autoencoders [2018]
- Automatic Fabric Defect Detection with a Multi-Scale Convolutional Denoising Autoencoder Network Model [Sensors 2018]
- An Unsupervised-Learning-Based Approach for Automated Defect Inspection on Textured Surfaces [TIM 2018]
- Unsupervised anomaly detection using style distillation [2020]
- Unsupervised two-stage anomaly detection [2021]
- Dfr: Deep feature reconstruction for unsupervised anomaly segmentation [Neurocomputing 2020]
- Unsupervised anomaly segmentation via multilevel image reconstruction and adaptive attention-level transition [2021]
- Encoding structure-texture relation with p-net for anomaly detection in retinal images [2020]
- Improved anomaly detection by training an autoencoder with skip connections on images corrupted with stain-shaped noise [2021]
- Unsupervised anomaly detection for surface defects with dual-siamese network [2022]
- Divide-and-assemble: Learning block-wise memory for unsupervised anomaly detection [ICCV 2021]
- Reconstruction from edge image combined with color and gradient difference for industrial surface anomaly detection [2022][code]
- Spatial Contrastive Learning for Anomaly Detection and Localization [2022]
- Superpixel masking and inpainting for self-supervised anomaly detection [BMVC 2020]
- Iterative image inpainting with structural similarity mask for anomaly detection [2020]
- Self-Supervised Masking for Unsupervised Anomaly Detection and Localization [2022]
- Reconstruction by inpainting for visual anomaly detection [PR 2021]
- Draem-a discriminatively trained reconstruction embedding for surface anomaly detection [ICCV 2021][code]
- DSR: A dual subspace re-projection network for surface anomaly detection [ECCV 2022][code]
- Natural Synthetic Anomalies for Self-supervised Anomaly Detection and Localization [ECCV 2022][code]
- Self-Supervised Training with Autoencoders for Visual Anomaly Detection [2022]
- Self-supervised predictive convolutional attentive block for anomaly detection [CVPR 2022 oral][code]
- Self-Supervised Masked Convolutional Transformer Block for Anomaly Detection [TPAMI 2022][code]
- Iterative energy-based projection on a normal data manifold for anomaly localization [2019]
- Towards visually explaining variational autoencoders [2020]
- Deep generative model using unregularized score for anomaly detection with heterogeneous complexity [2020]
- Anomaly localization by modeling perceptual features [2020]
- Image anomaly detection using normal data only by latent space resampling [2020]
- Noise-to-Norm Reconstruction for Industrial Anomaly Detection and Localization [2023]
- Patch-wise Auto-Encoder for Visual Anomaly Detection [2023]
- FAIR: Frequency-aware Image Restoration for Industrial Visual Anomaly Detection [2023][code]
- Template-guided Hierarchical Feature Restoration for Anomaly Detection [ICCV 2023]
- FastRecon: Few-shot Industrial Anomaly Detection via Fast Feature Reconstruction [ICCV 2023][code]
- Produce Once, Utilize Twice for Anomaly Detection [2023]
- RealNet: A Feature Selection Network with Realistic Synthetic Anomaly for Anomaly Detection [CVPR 2024][code]
- Implicit Foreground-Guided Network for Anomaly Detection and Localization [ICASSP 2024]
- Neural Network Training Strategy To Enhance Anomaly Detection Performance: A Perspective On Reconstruction Loss Amplification [ICASSP 2024]
- Patch-Wise Augmentation for Anomaly Detection and Localization [ICASSP 2024]
- A Reconstruction-Based Feature Adaptation for Anomaly Detection with Self-Supervised Multi-Scale Aggregation [ICASSP 2024]
- Neural Network Training Strategy To Enhance Anomaly Detection Performance: A Perspective On Reconstruction Loss Amplification [ICASSP 2024]
- Mixed-Attention Auto Encoder for Multi-Class Industrial Anomaly Detection [ICASSP 2024]
- Dual-Constraint Autoencoder and Adaptive Weighted Similarity Spatial Attention for Unsupervised Anomaly Detection [TII 2024]
- Multi-feature Reconstruction Network using Crossed-mask Restoration for Unsupervised Anomaly Detection [2024]
- R3D-AD: Reconstruction via Diffusion for 3D Anomaly Detection [ECCV 2024][homepage]
- Variational Autoencoder for Anomaly Detection: A Comparative Study [2024][code]
- Visual defect obfuscation based self-supervised anomaly detection [2024]
- Revitalizing Reconstruction Models for Multi-class Anomaly Detection via Class-Aware Contrastive Learning [2024][code]
- RcAE: Recursive Reconstruction Framework for Unsupervised Industrial Anomaly Detection [AAAI 2026]
2.2.2 Generative Adversarial Networks (GANs)
- Omni-frequency Channel-selection Representations for Unsupervised Anomaly Detection [TIP 2023][code]
- Learning semantic context from normal samples for unsupervised anomaly detection [AAAI 2021]
- Anoseg: Anomaly segmentation network using self-supervised learning [2021]
- A Surface Defect Detection Method Based on Positive Samples [PRICAI 2018]
- Few-shot defect image generation via defect-aware feature manipulation [AAAI 2023][code]
- CKAAD: Boosting Fine-Grained Visual Anomaly Detection with Coarse-Knowledge-Aware Adversarial Learning [AAAI 2025][code]
2.2.3 Transformer
- VT-ADL: A vision transformer network for image anomaly detection and localization [ISIE 2021]
- ADTR: Anomaly Detection Transformer with Feature Reconstruction [2022]
- AnoViT: Unsupervised Anomaly Detection and Localization With Vision Transformer-Based Encoder-Decoder [2022]
- HaloAE: An HaloNet based Local Transformer Auto-Encoder for Anomaly Detection and Localization [2022]
- Inpainting transformer for anomaly detection [ICIAP 2022]
- Masked Swin Transformer Unet for Industrial Anomaly Detection [2022]
- Masked Transformer for image Anomaly Localization [TII 2022]
- Focus the Discrepancy: Intra- and Inter-Correlation Learning for Image Anomaly Detection [ICCV 2023][code]
- AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and Localization [TASE 2024]
- Prior Normality Prompt Transformer for Multi-class Industrial Image Anomaly Detection [TII 2024]
- Context Enhancement with Reconstruction as Sequence for Unified Unsupervised Anomaly Detection[2024][code]
- Multi-scale feature reconstruction network for industrial anomaly detection [KBS 2024][code]
- Masked Autoencoder Self Pre-Training for Defect Detection in Microelectronics [2025]
- Vague Prototype-Oriented Diffusion Model for Multi-Class Anomaly Detection [ICML 2024]
- MC3D-AD: A Unified Geometry-aware Reconstruction Model for Multi-category 3D Anomaly Detection [IJCAI 2025]
2.2.4 Diffusion Model
- AnoDDPM: Anomaly Detection With Denoising Diffusion Probabilistic Models Using Simplex Noise [CVPR Workshop 2022]
- Unsupervised Visual Defect Detection with Score-Based Generative Model[2022]
- DiffusionAD: Denoising Diffusion for Anomaly Detection [2023][code]
- Anomaly Detection with Conditioned Denoising Diffusion Models [2023][code]
- Unsupervised Surface Anomaly Detection with Diffusion Probabilistic Model [ICCV 2023]
- Removing Anomalies as Noises for Industrial Defect Localization [ICCV 2023]
- TransFusion -- A Transparency-Based Diffusion Model for Anomaly Detection [ECCV 2024][code]
- LafitE: Latent Diffusion Model with Feature Editing for Unsupervised Multi-class Anomaly Detection [2023]
- DiAD: A Diffusion-based Framework for Multi-class Anomaly Detection [AAAI 2024][code]
- D3AD: Dynamic Denoising Diffusion Probabilistic Model for Anomaly Detection [2024]
- GLAD: Towards Better Reconstruction with Global and Local Adaptive Diffusion Models for Unsupervised Anomaly Detection [ECCV 2024][code]
- Tackling Structural Hallucination in Image Translation with Local Diffusion [ECCV 2024 oral][code]
- HDM: Hybrid Diffusion Model for Unified Image Anomaly Detection [2025]
- One-for-More: Continual Diffusion Model for Anomaly Detection [CVPR 2025]
- How and Why: Taming Flow Matching for Unsupervised Anomaly Detection and Localization [2025]
- InvAD: Inversion-based Reconstruction-Free Anomaly Detection with Diffusion Models [CVPR 2026][code]
- FDP: A Frequency-Decomposition Preprocessing Pipeline for Unsupervised Anomaly Detection in Brain MRI [AAAI 2026][code]
2.2.5 Others
- Anomaly Detection using Score-based Perturbation Resilience [ICCV 2023]
2.3 Supervised AD
More Normal Samples With (Less Abnormal Samples or Weak Labels)
- Neural batch sampling with reinforcement learning for semi-supervised anomaly detection [ECCV 2020]
- Explainable Deep One-Class Classification [ICLR 2020]
- Attention guided anomaly localization in images [ECCV 2020]
- Mixed supervision for surface-defect detection: From weakly to fully supervised learning [2021]
- Explainable deep few-shot anomaly detection with deviation networks [2021][code]
- Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detection [CVPR 2022][code]
- Anomaly Clustering: Grouping Images into Coherent Clusters of Anomaly Types[WACV 2023]
- Prototypical Residual Networks for Anomaly Detection and Localization [CVPR 2023][code]
- Efficient Anomaly Detection with Budget Annotation Using Semi-Supervised Residual Transformer [2023]
- Anomaly Heterogeneity Learning for Open-set Supervised Anomaly Detection [CVPR 2024][code]
- Few-shot defect image generation via defect-aware feature manipulation [AAAI 2023][code]
- AnomalyDiffusion: Few-Shot Anomaly Image Generation with Diffusion Model [AAAI 2024][code]
- BiaS: Incorporating Biased Knowledge to Boost Unsupervised Image Anomaly Localization [TSMC 2024]
- DMAD: Dual Memory Bank for Real-World Anomaly Detection [2024]
- AnomalousPatchCore: Exploring the Use of Anomalous Samples in Industrial Anomaly Detection [ECCVW 2024]
- SuperSimpleNet: Unifying Unsupervised and Supervised Learning for Fast and Reliable Surface Defect Detection [ICPR 2024][JIMS 2025][code]
- VarAD: Lightweight High-Resolution Image Anomaly Detection via Visual Autoregressive Modeling [TII 2025][code]
- Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection [CVPR 2025][code]
- Self-Tuning Self-Supervised Image Anomaly Detection [KDD 2025] [code]
More Abnormal Samples
- Logit Inducing With Abnormality Capturing for Semi-Supervised Image Anomaly Detection [2022]
- An effective framework of automated visual surface defect detection for metal parts [2021]
- Interleaved Deep Artifacts-Aware Attention Mechanism for Concrete Structural Defect Classification [TIP 2021]
- Reference-based defect detection network [TIP 2021]
- Fabric defect detection using tactile information [ICRA 2021]
- A lightweight spatial and temporal multi-feature fusion network for defect detection [TIP 2020]
- SDD-CNN: Small Data-Driven Convolution Neural Networks for Subtle Roller Defect Inspection [Robotics and Computer-Integrated Manufacturing 2020]
- A High-Efficiency Fully Convolutional Networks for Pixel-Wise Surface Defect Detection [IEEE Access 2019]
- SDD-CNN: Small Data-Driven Convolution Neural Networks for Subtle Roller Defect Inspection [Applied Sciences 2019]
- Autonomous Structural Visual Inspection Using Region-Based Deep Learning for Detecting Multiple Damage Types [CACIE 2018]
- Detection and segmentation of manufacturing defects with convolutional neural networks and transfer learning [2018]
- Automatic Metallic Surface Defect Detection and Recognition with Convolutional Neural Networks [Applied Sciences 2018]
- Real-time Detection of Steel Strip Surface Defects Based on Improved YOLO Detection Network [IFAC-PapersOnLine 2018]
- Domain adaptation for automatic OLED panel defect detection using adaptive support vector data description [IJCV 2017]
- Automatic Defect Detection of Fasteners on the Catenary Support Device Using Deep Convolutional Neural Network [TIM 2017]
- Deep Active Learning for Civil Infrastructure Defect Detection and Classification [Computing in civil engineering 2017]
- A fast and robust convolutional neural network-based defect detection model in product quality control [IJAMT 2017]
- Defects Detection Based on Deep Learning and Transfer Learning [Metallurgical & Mining Industry 2015]
- Design of deep convolutional neural network architectures for automated feature extraction in industrial inspection [CIRP annals 2016]
- Decision Fusion Network with Perception Fine-tuning for Defect Classification [2023]
- Global Context Aggregation Network for Lightweight Saliency Detection of Surface Defects [2023]
- Dual Attention U-Net with Feature Infusion: Pushing the Boundaries of Multiclass Defect Segmentation [2023][code]
- MemoryMamba: Memory-Augmented State Space Model for Defect Recognition [2024]
- Supervised Anomaly Detection for Complex Industrial Images [2024][code]
- Small Object Few-shot Segmentation for Vision-based Industrial Inspection [2024][code]
- SEM-CLIP: Precise Few-Shot Learning for Nanoscale Defect Detection in Scanning Electron Microscope Image [2025]
3 Other Research Direction
3.1 Zero/Few-Shot AD
Zero-Shot AD
- Random Word Data Augmentation with CLIP for Zero-Shot Anomaly Detection [BMVC 2023]
- Zero-Shot Batch-Level Anomaly Detection [2023]
- Zero-shot versus Many-shot: Unsupervised Texture Anomaly Detection [WACV 2023]
- MAEDAY: MAE for few and zero shot AnomalY-Detection [2022]
- WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation [CVPR 2023] [unofficial code in AnomalyCLIP] [unofficial code in SAA] [unofficial code in mala-lab]
- Segment Any Anomaly without Training via Hybrid Prompt Regularization [2023] [code]
- Anomaly Detection in an Open World by a Neuro-symbolic Program on Zero-shot Symbols [IROS 2022 Workshop]
- AnoVL: Adapting Vision-Language Models for Unified Zero-shot Anomaly Localization [2023][code]
- CLIP-AD: A Language-Guided Staged Dual-Path Model for Zero-shot Anomaly Detection [2023]
- PromptAD: Zero-shot Anomaly Detection using Text Prompts [WACV 2024]
- High-Fidelity Zero-Shot Texture Anomaly Localization Using Feature Correspondence Analysis [WACV 2024]
- AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detection [ICLR 2024][code]
- MuSc: Zero-Shot Industrial Anomaly Classification and Segmentation with Mutual Scoring of the Unlabeled Images[ICLR 2024][code][2025 v2]
- ClipSAM: CLIP and SAM Collaboration for Zero-Shot Anomaly Segmentation [2023]
- 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 [CVPRW 2023][code]
- Model Selection of Zero-shot Anomaly Detectors in the Absence of Labeled Validation Data [2024]
- Do LLMs Understand Visual Anomalies? Uncovering LLM Capabilities in Zero-shot Anomaly Detection [2024]
- FiLo: Zero-Shot Anomaly Detection by Fine-Grained Description and High-Quality Localization [2024]
- Dual-Image Enhanced CLIP for Zero-Shot Anomaly Detection [2024]
- Investigating the Semantic Robustness of CLIP-based Zero-Shot Anomaly Segmentation [2024]
- SAM-LAD: Segment Anything Model Meets Zero-Shot Logic Anomaly Detection [2024]
- VCP-CLIP: A visual context prompting model for zero-shot anomaly segmentation [ECCV 2024][code]
- AdaCLIP: Adapting CLIP with Hybrid Learnable Prompts for Zero-Shot Anomaly Detection [ECCV 2024][code]
- Towards Zero-shot Point Cloud Anomaly Detection: A Multi-View Projection Framework [2024]
- PointAD: Comprehending 3D Anomalies from Points and Pixels for Zero-shot 3D Anomaly Detection [NeurIPS 2024][code]
- VMAD: Visual-enhanced Multimodal Large Language Model for Zero-Shot Anomaly Detection [2024]
- GlocalCLIP: Object-agnostic Global-Local Prompt Learning for Zero-shot Anomaly Detection [2024]
- Towards Zero-shot 3D Anomaly Localization [WACV 2025]
- Towards Zero-Shot Anomaly Detection and Reasoning with Multimodal Large Language Models [2025][code]
- PA-CLIP: Enhancing Zero-Shot Anomaly Detection through Pseudo-Anomaly Awareness [2025]
- MFP-CLIP: Exploring the Efficacy of Multi-Form Prompts for Zero-Shot Industrial Anomaly Detection [2025]
- EIAD: Explainable Industrial Anomaly Detection Via Multi-Modal Large Language Models [2025]
- Crane: Context-Guided Prompt Learning and Attention Refinement for Zero-Shot Anomaly Detections [2025][code]
- AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection [AAAI 2026][code]
- MultiADS: Defect-aware Supervision for Multi-type Anomaly Detection and Segmentation in Zero-Shot Learning [ICCV 2025][code]
- AF-CLIP: Zero-Shot Anomaly Detection via Anomaly-Focused CLIP Adaptation [ACM MM 2025][code]
- CoPS: Conditional Prompt Synthesis for Zero-Shot Anomaly Detection [2025][code]
- IAD-R1: Reinforcing Consistent Reasoning in Industrial Anomaly Detection [ICCV 2025][code]
- On the Problem of Consistent Anomalies in Zero-Shot Industrial Anomaly Detection [TMLR 2025]
- FE-CLIP: Frequency Enhanced CLIP Model for Zero-Shot Anomaly Detection and Segmentation [ICCV 2025]
- Fine-grained Abnormality Prompt Learning for Zero-shot Anomaly Detection [ICCV 2025][code]
- MRAD: Zero-Shot Anomaly Detection with Memory-Driven Retrieval [ICLR 2026]
- PromptMoE: Generalizable Zero-Shot Anomaly Detection via Visually-Guided Prompt Mixtures [AAAI 2026][code]
- Exploring High-order-aware Prompt Learning for Zero-shot Anomaly Detection [AAAI 2026]
- DLVP-CLIP: Enhancing Fine-Grained Zero-Shot Anomaly Detection via Dynamic Local Visual Prompting [CVPR 2026]
- AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors [CVPR 2026][code]
- MoECLIP: Patch-Specialized Experts for Zero-shot Anomaly Detection [CVPR 2026][code]
- AG-VAS: Anchor-Guided Zero-Shot Visual Anomaly Segmentation with Large Multimodal Models [CVPR 2026][code]
- FB-CLIP: Fine-Grained Zero-Shot Anomaly Detection with Foreground-Background Disentanglement [CVPR 2026][code]
- From Attraction to Equilibrium: Physics-Inspired Semantic Gravitons for Zero-Shot Anomaly Detection [CVPR 2026]
Few-Shot AD
- Learning unsupervised metaformer for anomaly detection [ICCV 2021]
- Registration based few-shot anomaly detection [ECCV 2022 oral][code]
- Same same but differnet: Semi-supervised defect detection with normalizing flows [(Distribution)WACV 2021]
- Towards total recall in industrial anomaly detection [(Memory bank)CVPR 2022]
- A hierarchical transformation-discriminating generative model for few shot anomaly detection [ICCV 2021]
- Anomaly detection of defect using energy of point pattern features within random finite set framework [2021]
- Pushing the limits of fewshot anomaly detection in industry vision: Graphcore [ICLR 2023]
- Optimizing PatchCore for Few/many-shot Anomaly Detection [2023][code]
- AnomalyGPT: Detecting Industrial Anomalies using Large Vision-Language Models [AAAI 2024][code][project page]
- FastRecon: Few-shot Industrial Anomaly Detection via Fast Feature Reconstruction [ICCV 2023][code]
- Produce Once, Utilize Twice for Anomaly Detection [2023]
- COFT-AD: COntrastive Fine-Tuning for Few-Shot Anomaly Detection [TIP2024]
- Text-Guided Variational Image Generation for Industrial Anomaly Detection and Segmentation [CVPR 2024][code]
- Multimodal Industrial Anomaly Detection by Crossmodal Feature Mapping [CVPR 2024]
- Dual-path Frequency Discriminators for Few-shot Anomaly Detection [2024]
- Few-shot Online Anomaly Detection and Segmentation [2024]
- FewSOME: One-Class Few Shot Anomaly Detection with Siamese Networks [CVPRW 2023][code]
- AnomalyDINO: Boosting Patch-based Few-shot Anomaly Detection with DINOv2 [2024]
- Small Object Few-shot Segmentation for Vision-based Industrial Inspection [2024][code]
- Few-Shot Anomaly Detection via Category-Agnostic Registration Learning [2024][code]
- AnoPLe: Few-Shot Anomaly Detection via Bi-directional Prompt Learning with Only Normal Samples [2024][code]
- InCTRL: Toward Generalist Anomaly Detection via In-context Residual Learning with Few-shot Sample Prompts [CVPR 2024][code]
- FADE: Few-shot/zero-shot Anomaly Detection Engine using Large Vision-Language Model[BMVC 2024][code]
- FOCT: Few-shot Industrial Anomaly Detection with Foreground-aware Online Conditional Transport [ACM MM 2024]
- Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt [ECCV 2024][code]
- UniVAD: A Training-free Unified Model for Few-shot Visual Anomaly Detection [2024][code]
- SOWA: Adapting Hierarchical Frozen Window Self-Attention to Visual-Language Models for Better Anomaly Detection [2024][code]
- CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP [2024][code]
- PromptAD: Learning Prompts with only Normal Samples for Few-Shot Anomaly Detection [CVPR 2024][code]
- KAG-prompt: Kernel-Aware Graph Prompt Learning for Few-Shot Anomaly Detection [AAAI 2025][code]
- One-for-All Few-Shot Anomaly Detection via Instance-Induced Prompt Learning [ICLR 2025]
- SeaS: Few-shot Industrial Anomaly Image Generation with Separation and Sharing Fine-tuning [ICCV 2025][code]
- MetaUAS: Universal Anomaly Segmentation with One-Prompt Meta-Learning [NeurIPS 2024][code]
- One-to-Normal: Anomaly Personalization for Few-shot Anomaly Detection [NeurIPS 2024]
- Search is All You Need for Few-shot Anomaly Detection [2025]
- Foundation Visual Encoders Are Secretly Few-Shot Anomaly Detectors [2025][code]
- MetaCAN: Improving Generalizability of Few-shot Anomaly Detection with Meta-learning[CIKM 2025]
- UniADC: A Unified Framework for Anomaly Detection and Classification [2025]
- Commonality in Few: Few-Shot Multimodal Anomaly Detection via Hypergraph-Enhanced Memory [AAAI 2026][code]
- Towards Fine-Grained Vision-Language Alignment for Few-Shot Anomaly Detection [2025]
- Foundation Visual Encoders Are Secretly Few-Shot Anomaly Detectors [ICLR 2026][code]
- Dual Distillation for Few-Shot Anomaly Detection [ICLR 2026]
- CoPS: Conditional Prompt Synthesis for Zero-Shot Anomaly Detection [CVPR 2026][code]
- FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection [CVPR 2026]
- ADSeeker: A Knowledge-Grounded Reasoning Framework for Industry Anomaly Detection and Reasoning [CVPR 2026]
3.2 Noisy AD
- Trustmae: A noise-resilient defect classification framework using memory-augmented auto-encoders with trust regions [WACV 2021]
- Self-Supervise, Refine, Repeat: Improving Unsupervised Anomaly Detection [TMLR 2021]
- Data refinement for fully unsupervised visual inspection using pre-trained networks [2022]
- Latent Outlier Exposure for Anomaly Detection with Contaminated Data [ICML 2022]
- Deep one-class classification via interpolated gaussian descriptor [AAAI 2022 oral][code]
- SoftPatch: Unsupervised Anomaly Detection with Noisy Data [NeurIPS 2022][code]
- Inter-Realization Channels: Unsupervised Anomaly Detection Beyond One-Class Classification [ICCV 2023][code]
- M3DM-NR: RGB-3D Noisy-Resistant Industrial Anomaly Detection via Multimodal Denoising [2024]
- Meta Learning-Driven Iterative Refinement for Robust Anomaly Detection in Industrial Inspection [ECCVW 2024]
- SoftPatch+: Fully Unsupervised Anomaly Classification and Segmentation [PR 2025][code]
- FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data [WACV 2025][code]
- Towards Real Unsupervised Anomaly Detection Via Confident Meta-Learning [ICCV 2025]
3.3 Anomaly Synthesis [[awesome-anomaly-synthesis]](https://github.com/M-3LAB/awesome-anomaly-synthesis)
- Cutpaste: Self-supervised learning for anomaly detection and localization [(OCC)ICCV 2021][unofficial code]
- Draem-a discriminatively trained reconstruction embedding for surface anomaly detection [(Reconstruction AE)ICCV 2021][code]
- DSR: A dual subspace re-projection network for surface anomaly detection [ECCV 2022][code]
- Natural Synthetic Anomalies for Self-supervised Anomaly Detection and Localization [ECCV 2022][code]
- MemSeg: A semi-supervised method for image surface defect detection using differences and commonalities [(OCC)2022][unofficial code]
- A High-Efficiency Fully Convolutional Networks for Pixel-Wise Surface Defect Detection [IEEE Access 2019]
- Multistage GAN for fabric defect detection [2019]
- Gan-based defect synthesis for anomaly detection in fabrics [2020]
- Defect image sample generation with GAN for improving defect recognition [2020]
- Defective samples simulation through neural style transfer for automatic surface defect segment [2020]
- A simulation-based few samples learning method for surface defect segmentation [2020]
- Synthetic data augmentation for surface defect detection and classification using deep learning [2020]
- Defect Transfer GAN: Diverse Defect Synthesis for Data Augmentation [BMVC 2022]
- Defect-GAN: High-fidelity defect synthesis for automated defect inspection [2021]
- EID-GAN: Generative Adversarial Nets for Extremely Imbalanced Data Augmentation[TII 2022]
- Multilevel Saliency-Guided Self-Supervised Learning for Image Anomaly Detection [2023]
- DeSTSeg: Segmentation Guided Denoising Student-Teacher for Anomaly Detection [CVPR 2023][code]
- AnomalyDiffusion: Few-Shot Anomaly Image Generation with Diffusion Model [AAAI 2024][code]
- RealNet: A Feature Selection Network with Realistic Synthetic Anomaly for Anomaly Detection [CVPR 2024][code]
- Dual-path Frequency Discriminators for Few-shot Anomaly Detection [2024]
- A Novel Approach to Industrial Defect Generation through Blended Latent Diffusion Model with Online Adaptation [2024][code]
- A Comprehensive Augmentation Framework for Anomaly Detection [AAAI 2024]
- CAGEN: Controllable Anomaly Generator using Diffusion Model [ICASSP 2024]
- AnomalyXFusion: Multi-modal Anomaly Synthesis with Diffusion [2024][data]
- Few-shot defect image generation via defect-aware feature manipulation [AAAI 2023][code]
- A Unified Anomaly Synthesis Strategy with Gradient Ascent for Industrial Anomaly Detection and Localization [ECCV 2024][code]
- SLSG: Industrial Image Anomaly Detection with Improved Feature Embeddings and One-Class Classification [PR 2024]
- Dual-Modeling Decouple Distillation for Unsupervised Anomaly Detection [ACM MM 2024]
- SuperSimpleNet: Unifying Unsupervised and Supervised Learning for Fast and Reliable Surface Defect Detection [ICPR 2024][JIMS 2025][code]
- AnomalyControl: Learning Cross-modal Semantic Features for Controllable Anomaly Synthesis [2024]
- Progressive Boundary Guided Anomaly Synthesis for Industrial Anomaly Detection [TCSVT 2024][code]
- Few-Shot Anomaly-Driven Generation for Anomaly Classification and Segmentation [ECCV 2024][code]
- Component-aware Unsupervised Logical Anomaly Generation for Industrial Anomaly Detection [2025]
- "Stones from Other Hills can Polish Jade": Zero-shot Anomaly Image Synthesis via Cross-domain Anomaly Injection [2025]
- Fully-Synthetic Training for Visual Quality Inspection in Automotive Production [CIRP 2025]
- Enhanced Fabric Defect Detection with Feature Contrast Interference Suppression [TIM 2025]
- Open-Set Fabric Defect Detection With Defect Generation and Transfer [TIM 2025]
- Bounding Box-Guided Diffusion for Synthesizing Industrial Images and Segmentation Map [CVPRW 2025][code]
- Enhancing Glass Defect Detection with Diffusion Models: Addressing Imbalanced Datasets in Manufacturing Quality Control [2025]
- Photovoltaic Defect Image Generator with Boundary Alignment Smoothing Constraint for Domain Shift Mitigation [2025]
- Anomaly Anything: Promptable Unseen Visual Anomaly Generation [CVPR 2025][code]
- Anodapter: A Unified Framework for Generating Aligned Anomaly Images and Masks Using Diffusion Models[2025]
- AnomalyHybrid: A Domain-agnostic Generative Framework for General Anomaly Detection [CVPR 2025 SyntaGen Workshop]
- SynSpill: Improved Industrial Spill Detection With Synthetic Data [ICCVW 2025 oral][code][homepage]
- DictAS: A Framework for Class-Generalizable Few-Shot Anomaly Segmentation via Dictionary Lookup [ICCV 2025][code]
- AnoStyler: Text-Driven Localized Anomaly Generation via Lightweight Style Transfer [AAAI 2026][code]
- UniADC: A Unified Framework for Anomaly Detection and Classification [2025][code is comming]
- Anomagic: Crossmodal Prompt-driven Zero-shot Anomaly Generation [AAAI 2026][code]
- AnomalyControl: Highly-Aligned Anomalous Image Generation with Controlled Diffusion Model [ACM MM 2025]
- ASBench: Image Anomalies Synthesis Benchmark for Anomaly Detection [TAI 2026][code]
- CHIMERA: Controllable High-quality Image-Mask Extraction for Reliable Diffusion-Based Anomaly Synthesis [AAAI 2026][code]
- AnomalyPainter: Vision-Language-Diffusion Synergy for Zero-Shot Realistic and Diverse Industrial Anomaly Synthesis [AAAI 2026]
- CADiff: Context-Aware Diffusion for Controllable Anomaly Generation in Anomaly Detection [AAAI 2026]
- Quality-Aware Language-Conditioned Local Auto-Regressive Anomaly Synthesis and Detection [AAAI 2026][code]
- One-to-More: High-Fidelity Training-Free Anomaly Generation with Attention Control [CVPR 2026][code]
3.4 RGBD AD
- Anomaly detection in 3d point clouds using deep geometric descriptors [WACV 2022]
- Back to the feature: classical 3d features are (almost) all you need for 3D anomaly detection [2022][code]
- Anomaly Detection Requires Better Representations [2022]
- Asymmetric Student-Teacher Networks for Industrial Anomaly Detection [WACV 2022]
- Multimodal Industrial Anomaly Detection via Hybrid Fusion [CVPR 2023][code]
- Complementary Pseudo Multimodal Feature for Point Cloud Anomaly Detection [2023][code]
- Image-Pointcloud Fusion based Anomaly Detection using PD-REAL Dataset [2023][data]
- Towards Scalable 3D Anomaly Detection and Localization: A Benchmark via 3D Anomaly Synthesis and A Self-Supervised Learning Network [CVPR 2024][code]
- Shape-Guided Dual-Memory Learning for 3D Anomaly Detection [ICML 2023]
- EasyNet: An Easy Network for 3D Industrial Anomaly Detection [ACM MM 2023]
- Self-supervised Feature Adaptation for 3D Industrial Anomaly Detection [2024]
- Cheating Depth: Enhancing 3D Surface Anomaly Detection via Depth Simulation [WACV 2024][code]
- Incremental Template Neighborhood Matching for 3D anomaly detection [Neurocomputing 2024]
- Keep DRΓMing: Discriminative 3D anomaly detection through anomaly simulation [PRL 2024]
- Rethinking Reverse Distillation for Multi-Modal Anomaly Detection [AAAI 2024]
- Multimodal Industrial Anomaly Detection by Crossmodal Feature Mapping [CVPR 2024]
- Cross-Modal Distillation in Industrial Anomaly Detection: Exploring Efficient Multi-Modal IAD [2024][code]
- M3DM-NR: RGB-3D Noisy-Resistant Industrial Anomaly Detection via Multimodal Denoising [2024]
- Learning Diffusion Models for Multi-View Anomaly Detection [ECCV 2024]
- Towards Zero-shot 3D Anomaly Localization [WACV 2025]
- Revisiting Multimodal Fusion for 3D Anomaly Detection from an Architectural Perspective [AAAI 2025]
- Mentor3AD: Feature Reconstruction-based 3D Anomaly Detection via Multi-modality Mentor Learning [2025]
- AnomalyHybrid: A Domain-agnostic Generative Framework for General Anomaly Detection [CVPR 2025 SyntaGen Workshop]
- Commonality in Few: Few-Shot Multimodal Anomaly Detection via Hypergraph-Enhanced Memory [AAAI 2026][code]
- G2SF: Geometry-Guided Score Fusion for Multimodal Industrial Anomaly Detection[ICCV 2025][code]
- Unsupervised Multi-View Visual Anomaly Detection via Progressive Homography-Guided Alignment [AAAI 2026]
- RPE-PAD: Relative Pose Estimation for Pose-agnostic Anomaly Detection [AAAI 2026]
3.5 3D AD
- Real3D-AD: A Dataset of Point Cloud Anomaly Detection [NeurIPS 2023][code]
- PointCore: Efficient Unsupervised Point Cloud Anomaly Detector Using Local-Global Features [2024]
- Towards Scalable 3D Anomaly Detection and Localization: A Benchmark via 3D Anomaly Synthesis and A Self-Supervised Learning Network [CVPR 2024][code]
- R3D-AD: Reconstruction via Diffusion for 3D Anomaly Detection [ECCV 2024][homepage]
- Towards High-resolution 3D Anomaly Detection via Group-Level Feature Contrastive Learning [ACM MM 2024][code]
- Complementary Pseudo Multimodal Feature for Point Cloud Anomaly Detection [PR 2024] [code]
- Towards Zero-shot Point Cloud Anomaly Detection: A Multi-View Projection Framework [2024][code]
- Multi-Sensor Object Anomaly Detection: Unifying Appearance, Geometry, and Internal Properties [CVPR 2025][code]
- PointAD: Comprehending 3D Anomalies from Points and Pixels for Zero-shot 3D Anomaly Detection [NeurIPS 2024][code]
- Look Inside for More: Internal Spatial Modality Perception for 3D Anomaly Detection [AAAI 2025][code]
- Exploiting Point-Language Models with Dual-Prompts for 3D Anomaly Detection [2025]
- Fence Theorem: Preprocessing is Dual-Objective Semantic Structure Isolator in 3D Anomaly Detection [2025]
- Odd-One-Out: Anomaly Detection by Comparing with Neighbors [CVPR 2025]
- PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly Detection [CVPR 2025]
- MC3D-AD: A Unified Geometry-aware Reconstruction Model for Multi-category 3D Anomaly Detection [IJCAI 2025]
- Examining the Source of Defects from a Mechanical Perspective for 3D Anomaly Detection [2025][code]
- Bridging 3D Anomaly Localization and Repair via High-Quality Continuous Geometric Representation [[2025]](https://arxiv.org/pdf/2505.24431v1
- Taming Anomalies with Down-Up Sampling Networks: Group Center Preserving Reconstruction for 3D Anomaly Detection [ACM MM 2025]
- 3D-ADAM: A Dataset for 3D Anomaly Detection in Advanced Manufacturing [2025][data][code]
- 3DKeyAD: High-Resolution 3D Point Cloud Anomaly Detection via Keypoint-Guided Point Clustering [2025]
- Registration is a Powerful Rotation-Invariance Learner for 3D Anomaly Detection [NeurIPS 2025]
- IEC3D-AD: A 3D Dataset of Industrial Equipment Components for Unsupervised Point Cloud Anomaly Detection [2025]
- Towards High-Resolution 3D Anomaly Detection: A Scalable Dataset and Real-Time Framework for Subtle Industrial Defects [AAAI 2026 oral][code]
- Point Cloud Segmentation of Integrated Circuits Package Substrates Surface Defects Using Causal Inference: Dataset Construction and Methodology [2025][code is comming]
- Multi-View Reconstruction with Global Context for 3D Anomaly Detection [IEEE SMC 2025][code]
- Robust Modality-Incomplete Anomaly Detection: A Modality-Instructive Framework with Benchmark [ACM MM 2025]
- Bridging 3D Anomaly Localization and Repair via High-Quality Continuous Geometric Representation [ICCV 2025][code]
- PIRN: Prototypical-based Intra-modal Reconstruction with Normality Communication for Multi-modal Anomaly Detection [ICLR 2026]
- CASL: Curvature-Augmented Self-supervised Learning for 3D Anomaly Detection [AAAI 2026][code]
- SCoNE: Spherical Consistent Neighborhoods Ensemble for Effective and Efficient Multi-View Anomaly Detection [AAAI 2026]
- Back to Point: Exploring Point-Language Models for Zero-Shot 3D Anomaly Detection [CVPR 2026][code]
- Wavelet-Driven 3D Anomaly Detection under Pose-Agnostic and Sparse-View [CVPR 2026]
3.6 Continual AD
- Towards Total Online Unsupervised Anomaly Detection and Localization in Industrial Vision [2023]
- Towards Continual Adaptation in Industrial Anomaly Detection [ACM MM 2022]
- Unsupervised Continual Anomaly Detection with Contrastively-learned Prompt [AAAI 2024][code]
- An Incremental Unified Framework for Small Defect Inspection [ECCV2024][code]
- One-for-More: Continual Diffusion Model for Anomaly Detection [CVPR 2025]
- Memory Efficient Continual Learning for Edge-Based Visual Anomaly Detection [2025][code]
- ReplayCAD: Generative Diffusion Replay for Continual Anomaly Detection [IJCAI 2025][code]
- C3D-AD: Toward Continual 3D Anomaly Detection via Kernel Attention with Learnable Advisor [2025][code]
- CADIC: Continual Anomaly Detection Based on Incremental Coreset [2025]
- Exploring Multimodal Prompts For Unsupervised Continuous Anomaly Detection [ACM MM 2025]
- Complementary Prototype Mapping for Efficient Multimodal Anomaly Detection [CVPR 2026]
- GPFlow: Gaussian Prototype Probability Flow for Unsupervised Multi-Modal Anomaly Detection [CVPR 2026]
- Hierarchical Point-Patch Fusion with Adaptive Patch Codebook for 3D Shape Anomaly Detection [CVPR 2026][code]
- GS-CLIP: Zero-shot 3D Anomaly Detection by Geometry-Aware Prompt and Synergistic View Representation Learning [CVPR 2026][code]
- Geometry-Aligned and Anomaly-Aware Reconstruction for 3D Anomaly Detection [CVPR 2026]
- A Semantically Disentangled Unified Model for Multi-category 3D Anomaly Detection [CVPR 2026][code]
3.7 Uniform/Multi-Class AD
- A Unified Model for Multi-class Anomaly Detection [NeurIPS 2022] [code]
- OmniAL A unifiled CNN framework for unsupervised anomaly localization [CVPR 2023]
- SelFormaly: Towards Task-Agnostic Unified Anomaly Detection[2023]
- Hierarchical Vector Quantized Transformer for Multi-class Unsupervised Anomaly Detection [NeurIPS 2023][code]
- Removing Anomalies as Noises for Industrial Defect Localization [ICCV 2023]
- UniFormaly: Towards Task-Agnostic Unified Framework for Visual Anomaly Detection [2023][code]
- MSTAD: A masked subspace-like transformer for multi-class anomaly detection [2023]
- LafitE: Latent Diffusion Model with Feature Editing for Unsupervised Multi-class Anomaly Detection [2023]
- DiAD: A Diffusion-based Framework for Multi-class Anomaly Detection [AAAI 2024][code]
- Structural Teacher-Student Normality Learning for Multi-Class Anomaly Detection and Localization [2024]
- Unsupervised anomaly detection and localization with one model for all category [KBS 2024]
- Anomaly Detection by Adapting a pre-trained Vision Language Model [2024]
- DMAD: Dual Memory Bank for Real-World Anomaly Detection [2024]
- Toward Multi-class Anomaly Detection: Exploring Class-aware Unified Model against Inter-class Interference [2024]
- Hierarchical Gaussian Mixture Normalizing Flow Modeling for Unified Anomaly Detection [ECCV 2024][code]
- Continuous Memory Representation for Anomaly Detection [ECCV 2024][homepage][code]
- Long-Tailed Anomaly Detection with Learnable Class Names [CVPR 2024][data split]
- MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection [NeurIPS 2024][code]
- Learning Feature Inversion for Multi-class Anomaly Detection under General-purpose COCO-AD Benchmark [2024][code]
- Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detection [CVPR 2025][code]
- Prior Normality Prompt Transformer for Multi-class Industrial Image Anomaly Detection [TII 2024]
- An Incremental Unified Framework for Small Defect Inspection [ECCV2024][code]
- MoEAD: A Parameter-efficient Model for Multi-class Anomaly Detection [ECCV 2024][code]
- Learning Multi-view Anomaly Detection [2024]
- Revitalizing Reconstruction Models for Multi-class Anomaly Detection via Class-Aware Contrastive Learning [2024][code]
- ResAD: A Simple Framework for Class Generalizable Anomaly Detection [NeurIPS 2024][code]
- Exploring Plain ViT Reconstruction for Multi-class Unsupervised Anomaly Detection [CVIU 2025][code]
- Exploiting Point-Language Models with Dual-Prompts for 3D Anomaly Detection [2025]
- UniNet: A Contrastive Learning-guided Unified Framework with Feature Selection for Anomaly Detection [CVPR 2025][code coming soon]
- Boosting Global-Local Feature Matching via Anomaly Synthesis for Multi-Class Point Cloud Anomaly Detection [TASE 2025] [code]
- MC3D-AD: A Unified Geometry-aware Reconstruction Model for Multi-category 3D Anomaly Detection [IJCAI 2025][code]
- Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly Detection [TII 2025][code]
- VLMDiff: Leveraging Vision-Language Models for Multi-Class Anomaly Detection with Diffusion [2025][code]
- Learning Invariant Discriminative Patterns for Unified Anomaly Detection [ACM MM 2025]
- DecAD: Decoupling Anomalies in Latent Space for Multi-Class Unsupervised Anomaly Detection [ICCV 2025]
- Collaborative Reconstruction and Repair for Multi-class Industrial Anomaly Detection [Data Intelligence 2025][code]
- MaskAD: Parallel Masked Autoencoder for Multi-class Unsupervised Anomaly Detection [AAAI 2026][code]
3.8 Logical AD
- Beyond Dents and Scratches: Logical Constraints in Unsupervised Anomaly Detection and Localization [IJCV 2022]
- Set Features for Fine-grained Anomaly Detection[2023] [code]
- EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies [WACV 2024]
- Contextual Affinity Distillation for Image Anomaly Detection [WACV 2024]
- REB: Reducing Biases in Representation for Industrial Anomaly Detection [2023][code]
- Learning Global-Local Correspondence with Semantic Bottleneck for Logical Anomaly Detection [TCSVT 2023][code]
- Template-guided Hierarchical Feature Restoration for Anomaly Detection [ICCV 2023]
- Few Shot Part Segmentation Reveals Compositional Logic for Industrial Anomaly Detection [AAAI 2024][code]
- Generating and Reweighting Dense Contrastive Patterns for Unsupervised Anomaly Detection [AAAI 2024]
- PUAD: Frustratingly Simple Method for Robust Anomaly Detection [2024]
- AnomalyXFusion: Multi-modal Anomaly Synthesis with Diffusion [2024][data]
- Supervised Anomaly Detection for Complex Industrial Images [2024][code]
- SAM-LAD: Segment Anything Model Meets Zero-Shot Logic Anomaly Detection [2024]
- SLSG: Industrial Image Anomaly Detection with Improved Feature Embeddings and One-Class Classification [PR 2024]
- LogiCode: an LLM-Driven Framework for Logical Anomaly Detection [2024]
- CSAD: Unsupervised Component Segmentation for Logical Anomaly Detection [BMVC 2024][code]
- Revisiting Deep Feature Reconstruction for Logical and Structural Industrial Anomaly Detection[TMLR 2024][code]
- LogicAD: Explainable Anomaly Detection via VLM-based Text Feature Extraction [AAAI 2025][code]
- Component-aware Unsupervised Logical Anomaly Generation for Industrial Anomaly Detection [2025]
- Towards Training-free Anomaly Detection with Vision and Language Foundation Models [CVPR 2025][code]
- SALAD -- Semantics-Aware Logical Anomaly Detection [ICCV 2025][code]
- Uniad: Integrating geometric and semantic cues for unified anomaly detection [ACM MM 2025]
- Logical Anomaly Detection with Text-based Logic via Component-Aware Contrastive Language-Image Training [KDD 25]
3.9 VQA
- AnomalyGPT: Detecting Industrial Anomalies using Large Vision-Language Models [AAAI 2024][code][project page]
- Towards Generic Anomaly Detection and Understanding: Large-scale Visual-linguistic Model (GPT-4V) Takes the Lead [2023][code]
- Exploring Grounding Potential of VQA-oriented GPT-4V for Zero-shot Anomaly Detection [IJCAI WORKSHOP 2024][code]
- Customizing Visual-Language Foundation Models for Multi-modal Anomaly Detection and Reasoning [CSCWD 2024]
- Do LLMs Understand Visual Anomalies? Uncovering LLM Capabilities in Zero-shot Anomaly Detection [ACM MM 2024]
- LogiCode: an LLM-Driven Framework for Logical Anomaly Detection [T-ASE 2024]
- FabGPT: An Efficient Large Multimodal Model for Complex Wafer Defect Knowledge Queries [ICCAD 2024]
- VMAD: Visual-enhanced Multimodal Large Language Model for Zero-Shot Anomaly Detection [T-ASE][2024]
- Are Anomaly Scores Telling the Whole Story? A Benchmark for Multilevel Anomaly Detection [2024]
- MMAD: The Comprehensive Benchmark for Multimodal Large Language Models in Industrial Anomaly Detection [ICLR 2025][Code] [Data]
- Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection? [CIE 2025]
- EIAD: Explainable Industrial Anomaly Detection Via Multi-Modal Large Language Models [ICME 2025]
- Towards Zero-Shot Anomaly Detection and Reasoning with Multimodal Large Language Models [CVPR 2025][code]
- AnomalyR1: A GRPO-based End-to-end MLLM for Industrial Anomaly Detection [2025]
- Detect, Classify, Act: Categorizing Industrial Anomalies with Multi-Modal Large Language Models [VAND 2025]
- Triad: Empowering LMM-based Anomaly Detection with Vision Expert-guided Visual Tokenizer and Manufacturing Process [ICCV 2025]
- LR-IAD: Mask-Free Industrial Anomaly Detection with Logical Reasoning [2025]
- OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning [2025]
- EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware [2025][code]
- IAD-R1: Reinforcing Consistent Reasoning in Industrial Anomaly Detection [ICCV 2025][code]
- AD-FM: Multimodal LLMs for Anomaly Detection via Multi-Stage Reasoning and Fine-Grained Reward Optimization [AAAI 2026]
- AgentIAD: Tool-Augmented Single-Agent for Industrial Anomaly Detection [2025]
- Judo: A Juxtaposed Domain-oriented Multimodal Reasoner for Industrial Anomaly QA [ICLR 2026]
- Reason-IAD: Knowledge-Guided Dynamic Latent Reasoning for Explainable Industrial Anomaly Detection [2026][code]
- MAU-GPT: Enhancing Multi-type Industrial Anomaly Understanding via Anomaly-aware and Generalist Experts Adaptation [AAAI 2026]
- SAGE: A Visual Language Model for Anomaly Detection via Fact Enhancement and Entropy-aware Alignment [2026]
- IAD-Unify: A Region-Grounded Unified Model for Industrial Anomaly Segmentation, Understanding, and Generation [2026]
- Reasoning-Driven Anomaly Detection and Localization with Image-Level Supervision [CVPR 2026][code]
- MMR-AD: A Large-Scale Multimodal Dataset for Benchmarking General Anomaly Detection with Multimodal Large Language Models [CVPR 2026][code]
- AD-Copilot: A Vision-Language Assistant for Industrial Anomaly Detection via Visual In-context Comparison [2026][Code][Model][Demo]
Other settings
TTT binary segmentation
- Test Time Training for Industrial Anomaly Segmentation [2024]
MoE with TTA
- Adapted-MoE: Mixture of Experts with Test-Time Adaption for Anomaly Detection[2024][code coming soon]
Adversary Attack
- Adversarially Robust Industrial Anomaly Detection Through Diffusion Model [2024]
- Adversarially Robust Anomaly Detection through Spurious Negative Pair Mitigation [ICLR 2025]
Defect Classification
- AnomalyNCD: Towards Novel Anomaly Class Discovery in Industrial Scenarios [2024][code coming soon]
- MVREC: A General Few-shot Defect Classification Model Using Multi-View Region-Context [AAAI 2025]
- Defect Cue-Preserved Structural Feature Refinement for Few-Shot Anomaly Detection [CVPR 2026]
- UniSpector: Towards Universal Open-set Defect Recognition via Spectral-Contrastive Visual Prompting [CVPR 2026]
- Towards Open-Vocabulary Industrial Defect Understanding with a Large-Scale Multimodal Dataset [CVPR 2026][data]
Rubustness
- FiCo: Filter or Compensate: Towards Invariant Representation from Distribution Shift for Anomaly Detection [AAAI 2025][code]
Universal Task
- AnomalyMoE: Towards a Language-free Generalist Model for Unified Visual Anomaly Detection [AAAI 2025][code]
- UniMMAD: Unified Multi-Modal and Multi-Class Anomaly Detection via MoE-Driven Feature Decompression [CVPR 2026][code]
- Unified Unsupervised Anomaly Detection via Matching Cost Filtering [2025][code]
- One Dinomaly2 Detect Them All: A Unified Framework for Full-Spectrum Unsupervised Anomaly Detection [2025]
- UniADC: A Unified Framework for Anomaly Detection and Classification [2025][code is comming]
4 Dataset
| Dataset | Class | Normal | Abnormal | Total | Annotation level | Source | Time |
|---|---|---|---|---|---|---|---|
| 3CAD | 8 | 15577 | 11462 | 27039 | Segmentation mask | RGB real | AAAI, 2025 |
| AITEX | 1 | 140 | 105 | 245 | Segmentation mask | RGB real | 2019 |
| Anomaly-ShapeNet | 40 | - | - | 1600 | Point-level mask | Point-cloud synthetic | CVPR,2024 |
| BTAD | 3 | - | - | 2830 | Segmentation mask | RGB real | 2021 |
| CID | 1 | 4060 | 233 | 4293 | Segmentation mask | RGB real | 2024,TIM |
| DAGM | 10 | - | - | 11500 | Segmentation mask | RGB synthetic | 2007 |
| DEEPPCB | 1 | - | - | 1500 | Bounding box | RGB synthetic | 2019 |
| DTD-Synthetic | 12 | - | - | - | Segmentation mask | RGB synthetic | WACV,2024 |
| Eyecandies | 10 | 13250 | 2250 | 15500 | Segmentation mask | RGBD synthetic image | ACCV,2022 |
| Fabirc dataset | 1 | 25 | 25 | 50 | Segmentation mask | RGB synthetic | PR,2016 |
| GDXray | 1 | 0 | 19407 | 19407 | Bounding box | RGB real | 2016 |
| IPAD | 16 | - | - | 597979 | Image | Video real&synthetic | 2024 |
| KolekrotSDD | 1 | 347 | 52 | 399 | Segmentation mask | RGB real | JIM,2019 |
| KolekrotSDD2 | 1 | 2979 | 356 | 3335 | Segmentation mask | RGB real | CiI,2021 |
| MCBT (Manufacturing Complex Background Texture) | - | 800 | 227 | 1027 | Segmentation mask | RGB real | CVPRW,2025 |
| MIAD | 7 | 87500 | 17500 | 105000 | Segmentation mask | RGB synthetic | 2023 |
| MPDD | 6 | 1064 | 282 | 1346 | Segmentation mask | RGB real | ICUMT,2021 |
| MTD | 1 | 952 | 392 | 1344 | Segmentation mask | RGB real | CASE,2018 |
| MVTec AD | 15 | 4096 | 1258 | 5354 | Segmentation mask | RGB real | CVPR,2019 |
| MVTec 3D-AD | 10 | 2904 | 948 | 3852 | Segmentation mask | RGB real | VISAPP,2021 |
| MVTec LOCO-AD | 5 | 2347 | 993 | 3340 | Segmentation mask | RGBD real | IJCV,2022 |
| NanoTwice | 1 | 5 | 40 | 45 | Segmentation mask | RGB real | TII,2016 |
| NEU surface defect | 1 | 0 | 1800 | 1800 | Bounding box | RGB real | 2013 |
| PAD | 20 | 5231 | 4902 | 10133 | Segmentation mask | RBG synthetic | NeurIPS,2023 |
| Real-IAD | 30 | 99721 | 51329 | 151050 | Segmentation mask | RGB real | CVPR,2024 |
| Real3D-AD | 12 | 652 | 602 | 1254 | Point-level mask | Point-cloud real | NeurIPS,2023 |
| RSDD | 2 | - | - | 195 | Segmentation mask | RGB real | 2017 |
| Steel defect detection | 1 | - | - | 18076 | Image | RGB real | 2019 |
| Steel tube dataset | 1 | 0 | 3408 | 3408 | Bounding box | RGB real | 2021 |
| VisA | 12 | 9621 | 1200 | 10821 | Segmentation mask | RGB real | ECCV,2022 |
| RAD | 4 | 213 | 1224 | 1224 | Segmentation mask | RGB real | CASE,2024 |
- (NEU surface defect dataset)A noise robust method based on completed local binary patterns for hot-rolled steel strip surface defects [2013] [data]
- (Steel tube dataset)Deep learning based steel pipe weld defect detection [2021] [data]
- (Steel defect dataset)Severstal: Steel Defect Detection [data 2019]
- (NanoTwice)Defect detection in SEM images of nanofibrous materials [TII 2016] [data]
- (GDXray)GDXray: The database of X-ray images for nondestructive testing [2015] [data]
- (DEEP PCB)Online PCB defect detector on a new PCB defect dataset [2019] [data]
- (PCBA-defect) A PCB Dataset for Defects Detection and Classification [2019][data]
- (CPLID) Insulator Data Set - Chinese Power Line Insulator Dataset [data]
- (Fabric dataset)Fabric inspection based on the Elo rating method [PR 2016]
- (KolektorSDD)Segmentation-based deep-learning approach for surface-defect detection [Journal of Intelligent Manufacturing] [data]
- (KolektorSDD2)Mixed supervision for surface-defect detection: From weakly to fully supervised learning [Computers in Industry 2021] [data]
- SensumSODF-dataset: Detection of surface defects on pharmaceutical solid oral dosage forms with convolutional neural networks[Neural Computing and Applications 2021][data]
- (RSDD)A hierarchical extractor-based visual rail surface inspection system [2017]
- (Eyecandies)The Eyecandies Dataset for Unsupervised Multimodal Anomaly Detection and Localization [ACCV 2022] [data]
- (MVTec AD)MVTec AD: A comprehensive real-world dataset for unsupervised anomaly detection [CVPR 2019] [IJCV 2021] [data]β¨β¨β¨
- (MVTec 3D-AD)The mvtec 3d-ad dataset for unsupervised 3d anomaly detection and localization [VISAPP 2021] [data]β¨β¨
- (MVTec LOCO-AD)Beyond Dents and Scratches: Logical Constraints in Unsupervised Anomaly Detection and Localization [IJCV 2022] [data]β¨β¨β¨
- (MPDD)Deep learning-based defect detection of metal parts: evaluating current methods in complex conditions [ICUMT 2021] [data]
- (MPDD2)Anomaly detection for real-world industrial applications: benchmarking recent self-supervised and pretrained methods [ICUMT 2022] [data]
- (BTAD)VT-ADL: A vision transformer network for image anomaly detection and localization [2021] [data]
- (VisA)SPot-the-Difference Self-supervised Pre-training for Anomaly Detection and Segmentation [ECCV 2022] [data]β¨β¨β¨
- (MTD)Surface defect saliency of magnetic tile [2020] [data]
- (DAGM)DAGM dataset [data 2007]
- (MIAD)Miad:A maintenance inspection dataset for unsupervised anomaly detection [2022] [data]β¨β¨
- CVPR 1st workshop on Vision-based InduStrial InspectiON [homepage] [data]
- (SSGD)SSGD: A smartphone screen glass dataset for defect detection [2023][github page]
- (AeBAD)Industrial Anomaly Detection with Domain Shift: A Real-world Dataset and Masked Multi-scale Reconstruction [2023] [data]
- VISION Datasets: A Benchmark for Vision-based InduStrial InspectiON [2023] [data]β¨β¨β¨
- PAD: A Dataset and Benchmark for Pose-agnostic Anomaly Detection [NeurIPS 2023]
- PKU-GoodsAD: A Supermarket Goods Dataset for Unsupervised Anomaly Detection and Segmentation [2023][data]β¨β¨
- Real3D-AD: A Dataset of Point Cloud Anomaly Detection [NeurIPS 2023][data]β¨β¨β¨
- InsPLAD: A Dataset and Benchmark for Power Line Asset Inspection in UAV Images [IJRS 2023][data]
- Image-Pointcloud Fusion based Anomaly Detection using PD-REAL Dataset [2023][data]
- CrashCar101: Procedural Generation for Damage Assessment [WACV 2024][data]
- Defect Spectrum: A Granular Look of Large-Scale Defect Datasets with Rich Semantics [ECCV 2024][data]
- (DTD-Synthetic) Zero-shot versus Many-shot: Unsupervised Texture Anomaly Detection [WACV 2023][data]
- Towards Scalable 3D Anomaly Detection and Localization: A Benchmark via 3D Anomaly Synthesis and A Self-Supervised Learning Network [CVPR 2024][data]
- Real-IAD: A Real-World Multi-view Dataset for Benchmarking Versatile Industrial Anomaly Detection [CVPR 2024][code][data]β¨β¨β¨
- Catenary Insulator Defects Detection: A Dataset and an Unsupervised Baseline [TIM 2024][code]
- IPAD: Industrial Process Anomaly Detection Dataset [2024][data]
- MVTec-Caption: AnomalyXFusion: Multi-modal Anomaly Synthesis with Diffusion [2024][data]
- Supervised Anomaly Detection for Complex Industrial Images [2024][data]
- PeanutAD: A Real-World Dataset for Anomaly Detection in Agricultural Product Processing Line [2024][data]
- The Woven Fabric Defect Detection (WFDD) dataset [2024][data]
- Texture-AD: An Anomaly Detection Dataset and Benchmark for Real Algorithm Development[2024][data]
- Multi-Sensor Object Anomaly Detection: Unifying Appearance, Geometry, and Internal Properties [CVPR 2025][code]β¨β¨
- CableInspect-AD: An Expert-Annotated Anomaly Detection Dataset [NeurIPS 2024][data]
- RAD: A Dataset and Benchmark for Real-Life Anomaly Detection with Robotic Observations [IJCV 2024][data]
- AD3: Introducing a score for Anomaly Detection Dataset Difficulty assessment using VIADUCT dataset [ECCV 2024][data]
- MMAD: The First-Ever Comprehensive Benchmark for Multimodal Large Language Models in Industrial Anomaly Detection [ICLR 2025] [data]β¨β¨β¨
- MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects [2024][data]β¨β¨
- (MCBT) Manufacturing Complex Background Texture: a real-world industrial anomaly dataset for challenging textured backgrounds, introduced in When Textures Deceive: Weakly Supervised Industrial Anomaly Detection with Adapted-Loss [CVPR 2025 W (VAND)][data]
- Are Anomaly Scores Telling the Whole Story? A Benchmark for Multilevel Anomaly Detection [2024]
- 3CAD: A Large-Scale Real-World 3C Product Dataset for Unsupervised Anomaly [AAAI 2025][data]β¨β¨
- Towards Zero-Shot Anomaly Detection and Reasoning with Multimodal Large Language Models [2025][data]
- Real-IAD D3: A Real-World 2D/Pseudo-3D/3D Dataset for Industrial Anomaly Detection [CVPR 2025]β¨β¨
- The MVTec AD 2 Dataset: Advanced Scenarios for Unsupervised Anomaly Detection [CVPR2025W Challenge][paper][data]β¨β¨
- Towards Visual Discrimination and Reasoning of Real-World Physical Dynamics: Physics-Grounded Anomaly Detection [CVPR 2025]β¨β¨
- CXR-AD: Component X-ray Image Dataset for Industrial Anomaly Detection [2025]
- Visual Anomaly Detection under Complex View-Illumination Interplay: A Large-Scale Benchmark [2025][data]β¨β¨
- Unsupervised Anomaly Segmentation at High Resolution with Patch-Divide-and-Conquer and Self-ensembling [ECCVW 2025][data]
- HSS-IAD: A Heterogeneous Same-Sort Industrial Anomaly Detection Dataset [ICME 2025][data]
- 3D-ADAM: A Dataset for 3D Anomaly Detection in Advanced Manufacturing [2025][data][code]
- Kaputt: A Large-Scale Dataset for Visual Defect Detection [ICCV 2025][data]
- Real-IAD Variety: Pushing Industrial Anomaly Detection Dataset to a Modern Era [2025][data is comming]
- Towards High-Resolution 3D Anomaly Detection: A Scalable Dataset and Real-Time Framework for Subtle Industrial Defects [AAAI 2026 oral][data]
- ADNet: A Large-Scale and Extensible Multi-Domain Benchmark for Anomaly Detection Across 380 Real-World Categories [2025][data is comming]
- Anomaly Detection of Integrated Circuits Package Substrates Using the Large Vision Model SAIC: Dataset Construction, Methodology, and Application [ICCV 2025][data]
- SiM3D: Single-instance Multiview Multimodal and Multisetup 3D Anomaly Detection Benchmark [ICCV 2025][data]
- Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts [ICCV 2025][data]
- Towards Open-Vocabulary Industrial Defect Understanding with a Large-Scale Multimodal Dataset [2025]
- MMR-AD: A Large-Scale Multimodal Dataset for Benchmarking General Anomaly Detection with Multimodal Large Language Models [CVPR 2026][code]
- ADSeeker: A Knowledge-Grounded Reasoning Framework for Industry Anomaly Detection and Reasoning [CVPR 2026]
- Omni-AD: A Large-scale and Versatile Benchmark for Industrial Anomaly Detection [CVPR 2026]
- Towards Open-Vocabulary Industrial Defect Understanding with a Large-Scale Multimodal Dataset [CVPR 2026][data]
BibTex Citation
If you find this paper and repository useful, please cite our paperβΊοΈ.
text
@article{liu2024deep,
title={Deep industrial image anomaly detection: A survey},
author={Liu, Jiaqi and Xie, Guoyang and Wang, Jinbao and Li, Shangnian and Wang, Chengjie and Zheng, Feng and Jin, Yaochu},
journal={Machine Intelligence Research},
volume={21},
number={1},
pages={104--135},
year={2024},
publisher={Springer}
}
@article{xie2024iad,
title={Im-iad: Industrial image anomaly detection benchmark in manufacturing},
author={Xie, Guoyang and Wang, Jinbao and Liu, Jiaqi and Lyu, Jiayi and Liu, Yong and Wang, Chengjie and Zheng, Feng and Jin, Yaochu},
journal={IEEE Transactions on Cybernetics},
year={2024},
publisher={IEEE}
}
@article{jiang2022survey,
title={A survey of visual sensory anomaly detection},
author={Jiang, Xi and Xie, Guoyang and Wang, Jinbao and Liu, Yong and Wang, Chengjie and Zheng, Feng and Jin, Yaochu},
journal={arXiv preprint arXiv:2202.07006},
year={2022}
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