M-3LAB/awesome-industrial-anomaly-detection

Paper list and datasets for industrial image anomaly/defect detection (updating). 工业异常/瑕疵检测论文及数据集检索库(持续更新)。

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updated Sep 20, 2026

link.springer.com/content/pdf/10.1007/s11633-023-1459-z.pdf

See the code

README

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)

A Survey of Recent Advances in Industrial Anomaly Detection: From Normal-Only Training to Foundation-Model Priors [2026]

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

🔥🔥🔥 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 [survey]
- ECCV 2026
- 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
Towards total recall in industrial anomaly detection	CVPR	2022	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
AD-Copilot: A Vision-Language Assistant for Industrial Anomaly Detection via Visual In-context Comparison	arxiv	2026	Github	MLLM
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	2025	Github	Benchmark
RobustMAD: Evaluating Real-World Robustness of Multimodal Small Language Models for Deployable Anomaly Detection Assistants	TMLR	2026	Github	Benchmark

Recent research [survey]

ECCV 2026

CMDS-AD: Cross-Modal Dual-Stream Decoupling for Few-Shot Anomaly Detection [ECCV 2026][code]
Defect-aware Hybrid Prompt Optimization for Zero-Shot Multi-type Anomaly Detection and Segmentation [ECCV 2026]
DeCo: Zero-Shot Anomaly Generation through Decoupling and Recoupling [ECCV 2026][code]
UniScale: Arbitrary-Scale Anomaly Generation [ECCV 2026][code]
PADFormer: Pose-agnostic Anomaly Detection from Sparse View Images [ECCV 2026 Oral]
ReFP-AD: Rectified Flow Preconditioning for Energy-Based Anomaly Detection [ECCV 2026][code]
IMMoE: Incomplete Multi-View Anomaly Detection via Mixture of View Experts Fusion [ECCV 2026][code]
O-VAD: Industrial Video Anomaly Detection through Object-Centric Tracking and Reasoning [ECCV 2026][code]
Global Logic and Local Search: Dual-Stream Multimodal In-Context Learning for Verifiable Industrial Anomaly Detection [ECCV 2026]
ArcAD: Anomaly-Rectified Calibration for Cold-Start Supervised Anomaly Detection [ECCV 2026][code]
Robust Zero-shot Anomaly Detection under Limited Auxiliary Anomaly Priors [ECCV 2026]
LogiCo: A Unified Framework for Logical and Structural Anomaly Detection [ECCV 2026][code]
DeCoFlow: Structural Decomposition of Normalizing Flows for Continual Anomaly Detection [ECCV 2026]
MATCH: Flow Matching for Multi-View Anomaly Detection [ECCV 2026]
Rethinking Continual Anomaly Detection on the Edge: Benchmarking Under Realistic Industrial Conditions [ECCV 2026][code]
FuDU: A Fuzzy Dual-dimension Uncertainty Framework for Streaming Active Learning in Industrial Defect Detection[ECCV 2026][code]
Proximity-CLIP: Text-Guided Semantic Proximity Learning for Zero-Shot Anomaly Detection [ECCV 2026][paper]
CL-Anomaly: Layer-Adaptive Mixture-of-Experts with Multimodal Large Language Model for Continual Learning in Anomaly Detection [ECCV 2026][code]
Anomaly Factory 3D: A Modular Framework for Diverse Pseudo-Anomaly Synthesis in Unsupervised 3D Anomaly Detection [ECCV 2026][code]
GroundingAnomaly: Spatially-Grounded Diffusion for Few-Shot Anomaly Synthesis [ECCV 2026]
BAAF: Universal Transformation of One-Class Classifiers for Unsupervised Image Anomaly Detection [ECCV 2026][code]
DeltaDeno: Zero-Shot Anomaly Generation via Delta-Denoising Attribution [ECCV 2026][code]
VarProtoAD: Variational Prototype-Conditioned Prompting for Zero-Shot Anomaly Detection [ECCV 2026]
DeMuS: Learning Decoupled Matching and Scoring for Batch Zero-Shot Industrial Anomaly Detection [ECCV 2026][code]
Fast Dynamic Prototypes for Unsupervised Anomaly Detection and Localization [ECCV 2026][code coming soon]
HLRAD: High-dimensional Latent Representation for Unified Anomaly Detection [ECCV 2026]
Beyond Common Sense: Grounding Logical Anomaly Detection in Inspection Criteria [ECCV 2026][code]
EGVLR: Evidence-Grounded Vision-Language Reinforcement for Anomaly Reasoning [ECCV 2026][code]
A Comprehensive Analysis about Unsupervised Outlier Detection for Images [ECCV 2026][code]

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]
Anomaly-Preference Image Generation [ICML 2026]
Mixture Prototype Flow Matching for Open-Set Supervised Anomaly Detection [ICML 2026][code]

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]
Real-IAD MVN: A Multi-View Normal Vector Dataset and Benchmark for High-Fidelity Industrial Anomaly Detection [CVPR 2026]

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 [AAAI 2026][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

Recent developments in industrial anomaly detection have reshaped method categories and research paradigms, as illustrated in the timeline below. [paper]

Paper list for industrial image anomaly detection

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]
A Survey of Recent Advances in Industrial Anomaly Detection: From Normal-Only Training to Foundation-Model Priors [2026]

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]
BAAF: Universal Transformation of One-Class Classifiers for Unsupervised Image Anomaly Detection [ECCV 2026][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]
Fast Dynamic Prototypes for Unsupervised Anomaly Detection and Localization [ECCV 2026][code coming soon]

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]
Mixture Prototype Flow Matching for Open-Set Supervised Anomaly Detection [ICML 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]
Mixture Prototype Flow Matching for Open-Set Supervised Anomaly Detection [ICML 2026][code]
ArcAD: Anomaly-Rectified Calibration for Cold-Start Supervised Anomaly Detection [ECCV 2026][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]
GroundingAnomaly: Spatially-Grounded Diffusion for Few-Shot Anomaly Synthesis [ECCV 2026]
DeltaDeno: Zero-Shot Anomaly Generation via Delta-Denoising Attribution [ECCV 2026][code]
Structured guided diffusion models for industrial defect image generation [KBS 2025][code]
ISP-AD: a large-scale real-world dataset for advancing industrial anomaly detection with synthetic and real defects [JIMS 2026][code][data]

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 [AAAI 2026][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]
Defect-aware Hybrid Prompt Optimization for Zero-Shot Multi-type Anomaly Detection and Segmentation [ECCV 2026]
Towards Active Real-to-Twin Inspection: A New Paradigm for Zero-Shot Anomaly Detection[IEEE-CYBER 2026]
Robust Zero-shot Anomaly Detection under Limited Auxiliary Anomaly Priors [ECCV 2026]
Proximity-CLIP: Text-Guided Semantic Proximity Learning for Zero-Shot Anomaly Detection [ECCV 2026][paper]
VarProtoAD: Variational Prototype-Conditioned Prompting for Zero-Shot Anomaly Detection [ECCV 2026]
DeMuS: Learning Decoupled Matching and Scoring for Batch Zero-Shot Industrial Anomaly Detection [ECCV 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]
CMDS-AD: Cross-Modal Dual-Stream Decoupling for Few-Shot Anomaly Detection [ECCV 2026][code]

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]
Structured guided diffusion models for industrial defect image generation [KBS 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]
Anomaly-Preference Image Generation [ICML 2026]
DeCo: Zero-Shot Anomaly Generation through Decoupling and Recoupling [ECCV 2026][code]
UniScale: Arbitrary-Scale Anomaly Generation [ECCV 2026][code]
ISP-AD: a large-scale real-world dataset for advancing industrial anomaly detection with synthetic and real defects [JIMS 2026][code][data]

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]
Anomaly Factory 3D: A Modular Framework for Diverse Pseudo-Anomaly Synthesis in Unsupervised 3D Anomaly Detection [ECCV 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]

Towards Active Real-to-Twin Inspection: A New Paradigm for Zero-Shot Anomaly Detection[IEEE-CYBER 2026]
PADFormer: Pose-agnostic Anomaly Detection from Sparse View Images [ECCV 2026 Oral]
IMMoE: Incomplete Multi-View Anomaly Detection via Mixture of View Experts Fusion [ECCV 2026][code]

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]
DeCoFlow: Structural Decomposition of Normalizing Flows for Continual Anomaly Detection [ECCV 2026]
Rethinking Continual Anomaly Detection on the Edge: Benchmarking Under Realistic Industrial Conditions [ECCV 2026][code]
CL-Anomaly: Layer-Adaptive Mixture-of-Experts with Multimodal Large Language Model for Continual Learning in Anomaly Detection [ECCV 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]
ReFP-AD: Rectified Flow Preconditioning for Energy-Based Anomaly Detection [ECCV 2026][code]
HLRAD: High-dimensional Latent Representation for Unified Anomaly Detection [ECCV 2026]

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]
VID-AD: A Dataset for Image-Level Logical Anomaly Detection under Vision-Induced Distraction [2026][data]
LogiCo: A Unified Framework for Logical and Structural Anomaly Detection [ECCV 2026][code]
Beyond Common Sense: Grounding Logical Anomaly Detection in Inspection Criteria [ECCV 2026][code]

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 [AAAI 2026][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]
Global Logic and Local Search: Dual-Stream Multimodal In-Context Learning for Verifiable Industrial Anomaly Detection [ECCV 2026]
EGVLR: Evidence-Grounded Vision-Language Reinforcement for Anomaly Reasoning [ECCV 2026][code]

3.10 Video IAD

Towards Visual Discrimination and Reasoning of Real-World Physical Dynamics: Physics-Grounded Anomaly Detection [CVPR 2025][code]
O-VAD: Industrial Video Anomaly Detection through Object-Centric Tracking and Reasoning [ECCV 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	Segmentatio

Truncated — view the full README on GitHub.

anomaly-detection

anomaly-segmentation

computer-vision

dataset

deep-learning

defect-detection

industrial-image

medical

Contributors

437 commits

118 commits

45 commits

24 commits

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