Awesome Industrial Anomaly Detection

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][中文]

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]

🔥🔥🔥 Contributions to our repository are welcome. Feel free to categorize the papers and pull requests.

🔥🔥🔥 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]

🔥🔥🔥 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]

🔥🔥🔥 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]

🔥🔥🔥 3D Anomaly Detection: A Survey [paper] [github]

Awesome Industrial Anomaly Detection
- Table of Contents
SOTA methods with code
Recommended Benchmarks
Recent research
- ICML 2026
- CVPR 2026
- ICLR 2026
- AAAI 2026
- NeurIPS 2025
- KDD 2025
- ICCV 2025
- ICML 2025
- CVPR 2025
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
- BibTex Citation
- Star History

SOTA methods with code

Title	Venue	Date	Code	topic
Anomaly Detection via Reverse Distillation from One-Class Embedding	CVPR	2022	Github	Teacher-Student
Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detection	CVPR	2025	Github	Multi-Class Unified
One Dinomaly2 Detect Them All: A Unified Framework for Full-Spectrum Unsupervised Anomaly Detection	Arxiv	2025	Github	Multi-Class, Multi-View, Multi-Modal, Few-shot
Revisiting Reverse Distillation for Anomaly Detection	CVPR	2023	Github	Teacher-Student
SimpleNet: A Simple Network for Image Anomaly Detection and Localization	CVPR	2023	Github	One-Class-Classification
Real-time unsupervised anomaly detection with localization via conditional normalizing flows	WACV	2022	Github	Distribution Map
PyramidFlow: High-Resolution Defect Contrastive Localization using Pyramid Normalizing Flow	CVPR	2023	Github	Distribution Map
Towards total recall in industrial anomaly detection	CVPR	2022	Github	Memory-bank
PNI: Industrial Anomaly Detection using Position and Neighborhood Information	ICCV	2023	Github	Memory-bank
Draem-a discriminatively trained reconstruction embedding for surface anomaly detection	ICCV	2021	Github	Reconstruction-based
DSR: A dual subspace re-projection network for surface anomaly detection	ECCV	2022	Github	Reconstruction-based
Omni-frequency Channel-selection Representations for Unsupervised Anomaly Detection	TIP	2023	Github	Reconstruction-based
RealNet: A Feature Selection Network with Realistic Synthetic Anomaly for Anomaly Detection	CVPR	2024	Github	Reconstruction-based
Registration based few-shot anomaly detection	ECCV	2022	Github	Few Shot
AnomalyGPT: Detecting Industrial Anomalies using Large Vision-Language Models	AAAI	2024	Github	Few Shot
Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detection	CVPR	2022	Github	Few abnormal samples
Explicit Boundary Guided Semi-Push-Pull Contrastive Learning for Supervised Anomaly Detection	CVPR	2023	Github	Few abnormal samples
Deep one-class classification via interpolated gaussian descriptor	AAAI	2022	Github	Noisy AD
SoftPatch: Unsupervised Anomaly Detection with Noisy Data	NeurIPS	2022	Github	Noisy AD
Inter-Realization Channels: Unsupervised Anomaly Detection Beyond One-Class Classification	ICCV	2023	Github	Noisy AD
Unsupervised Continual Anomaly Detection with Contrastively-learned Prompt	AAAI	2024	Github	Continual AD
A Unified Model for Multi-class Anomaly Detection	NeurIPS	2022	Github	Multi-class unified
Hierarchical Vector Quantized Transformer for Multi-class Unsupervised Anomaly Detection	NeurIPS	2023	Github	Multi-class unified
Multimodal Industrial Anomaly Detection via Hybrid Fusion	CVPR	2023	Github	RGBD
Real3D-AD: A Dataset of Point Cloud Anomaly Detection	NeurIPS	2023	Github	Point Cloud
AnoVL: Adapting Vision-Language Models for Unified Zero-shot Anomaly Localization	arxiv	2023	Github	Zero Shot
Segment Any Anomaly without Training via Hybrid Prompt Regularization	arxiv	2023	Github	Zero Shot
PSAD: Few Shot Part Segmentation Reveals Compositional Logic for Industrial Anomaly Detection	AAAI	2024	Github	Logical/Few Shot
SALAD -- Semantics-Aware Logical Anomaly Detection	ICCV	2025	Github	Logical
UniFormaly: Towards Task-Agnostic Unified Framework for Visual Anomaly Detection	arxiv	2023	Github	Multi-class unified

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

ICML 2026

Memory-Distilled Selection for Noise-Robust Anomaly Detection [ICML 2026][code]
CoGeoAD: Hierarchical Color-Geometric Fusion with Multi-View Attention for Zero-Shot 3D Anomaly Detection [ICML 2026]
Formally Exploring Visual Anomaly Detection Evaluation Metrics [ICML 2026]
Is Training Necessary for Anomaly Detection? [ICML 2026][code]
Remove the Ambiguity: Few-shot Multimodal Anomaly Detection Using Crossmodal Feature Replacers [ICML 2026]

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]
MRAD: Zero-Shot Anomaly Detection with Memory-Driven Retrieval [ICLR 2026][code]

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 Schrödinger 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)

PaperTree

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][中文]
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 student–teacher 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]
Memory-Distilled Selection for Noise-Robust Anomaly Detection [ICML 2026][code]

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] [code]
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]
SynSur: An end-to-end generative pipeline for synthetic industrial surface defect generation and detection [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]
MRAD: Zero-Shot Anomaly Detection with Memory-Driven Retrieval [ICLR 2026][code]
MuSc-V2: Zero-Shot Multimodal Industrial Anomaly Classification and Segmentation with Mutual Scoring of Unlabeled Samples [TPAMI 2026][code]

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]
Memory-Distilled Selection for Noise-Robust Anomaly Detection [ICML 2026][code]

3.3 Anomaly Synthesis [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 DRÆMing: 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]
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]
MuSc-V2: Zero-Shot Multimodal Industrial Anomaly Classification and Segmentation with Mutual Scoring of Unlabeled Samples [TPAMI 2026][code]
PIAD: Pose and Illumination agnostic Anomaly Detection [CVPR 2025] [code][data]

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 MLLM-based AD

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]
M3-AD: Reflection-aware Multi-modal, Multi-category, and Multi-dimensional Benchmark and Framework for Industrial Anomaly Detection [2026][code]
AD-Copilot: A Vision-Language Assistant for Industrial Anomaly Detection via Visual In-context Comparison [2026][Code][Model][Demo]
IndusAgent: Reinforcing Open-Vocabulary Industrial Anomaly Detection with Agentic Tools [2026]
AnomalyAgent: Training-Free Agentic Models for Zero-/Few-Shot Anomaly Detection [2026][code]

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] [code]
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]✨✨✨
(MVTec 3D-AD)The mvtec 3d-ad dataset for unsupervised 3d anomaly detection and localization [VISAPP 2021] [data]✨✨
(MVTec LOCO-AD)Beyond Dents and Scratches: Logical Constraints in Unsupervised Anomaly Detection and Localization [IJCV 2022] [data]✨✨✨
(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]✨✨✨
(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]✨✨
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]✨✨✨
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]✨✨
Real3D-AD: A Dataset of Point Cloud Anomaly Detection [NeurIPS 2023][data]✨✨✨
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]✨✨✨
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]✨✨
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]✨✨✨
MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects [2024][data]✨✨
(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]✨✨
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]✨✨
The MVTec AD 2 Dataset: Advanced Scenarios for Unsupervised Anomaly Detection [CVPR2025W Challenge][paper][data]✨✨
Towards Visual Discrimination and Reasoning of Real-World Physical Dynamics: Physics-Grounded Anomaly Detection [CVPR 2025]✨✨
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]✨✨
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]
Robust AD: A Real World Benchmark Dataset For Robustness in Industrial Anomaly Detection [CVPRW 2025][data]
PIAD: Pose and Illumination agnostic Anomaly Detection [CVPR 2025] [code][data]
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]
M3-AD: Reflection-aware Multi-modal, Multi-category, and Multi-dimensional Benchmark and Framework for Industrial Anomaly Detection [2026][code]

BibTex Citation

If you find this paper and repository useful, please cite our paper☺️.

@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}
}

awesome-industrial-anomaly-detection

About awesome-industrial-anomaly-detection

Platforms

Links

README.md