Awesome

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.

🔥🔥🔥 Which MLLM performs best in industrial anomaly detection? Please refer to our recent research, which evaluates state-of-the-art models, including GPT-4o, Gemini-1.5, LLaVA-Next, and InternVL.

[2024.10.16] We are proud to announce the launch of MMAD, the first-ever comprehensive benchmark for Multimodal Large Language Models in Industrial Anomaly Detection! 🌟 [Paper] [Code] [Data]

SOTA methods with code
Recommended Benchmarks
Recent research
Paper Tree (Classification of representative methods)
Time Line
Paper list for industrial image anomaly detection
Related Survey, Benchmark and Framework
2 Unsupervised AD
3 Other Research Direction
4 Dataset

SOTA methods with code

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

Recommended Benchmarks

Title	Venue	Date	Code	topic
<br> Anomalib: A Deep Learning Library for Anomaly Detection <br>	ICIP	2022	Github	Benchmark
<br> IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturing <br>	TCYB	2024	Github	Benchmark
<br> ADer: A Comprehensive Benchmark for Multi-class Visual Anomaly Detection <br>	arxiv	2024	Github	Benchmark
<br> MMAD: The First-Ever Comprehensive Benchmark for Multimodal Large Language Models in Industrial Anomaly Detection <br>	arxiv	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

NeurIPS 2024

MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection [NeurIPS 2024][code]
PointAD: Comprehending 3D Anomalies from Points and Pixels for Zero-shot 3D Anomaly Detection [NeurIPS 2024][code]
CableInspect-AD: An Expert-Annotated Anomaly Detection Dataset [NeurIPS 2024][data]
ResAD: A Simple Framework for Class Generalizable Anomaly Detection [NeurIPS 2024][code]

ECCV 2024

R3D-AD: Reconstruction via Diffusion for 3D Anomaly Detection [ECCV 2024][homepage]
An Incremental Unified Framework for Small Defect Inspection [ECCV2024][code]
Learning Unified Reference Representation for Unsupervised Multi-class Anomaly Detection [ECCV 2024][code]
Self-supervised Feature Adaptation for 3D Industrial Anomaly Detection [ECCV 2024]
Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt [ECCV 2024][code]
Few-Shot Anomaly-Driven Generation for Anomaly Classification and Segmentation [ECCV 2024][code]
AdaCLIP: Adapting CLIP with Hybrid Learnable Prompts for Zero-Shot Anomaly Detection [ECCV 2024][code]
GLAD: Towards Better Reconstruction with Global and Local Adaptive Diffusion Models for Unsupervised Anomaly Detection [ECCV 2024][code]
GeneralAD: Anomaly Detection Across Domains by Attending to Distorted Features [ECCV 2024][code]
VCP-CLIP: A visual context prompting model for zero-shot anomaly segmentation [ECCV 2024][code]
A Unified Anomaly Synthesis Strategy with Gradient Ascent for Industrial Anomaly Detection and Localization [ECCV 2024][code]
Hierarchical Gaussian Mixture Normalizing Flow Modeling for Unified Anomaly Detection [ECCV 2024][code]
TransFusion -- A Transparency-Based Diffusion Model for Anomaly Detection [ECCV 2024][code]
Continuous Memory Representation for Anomaly Detection [ECCV 2024][homepage][code]
Defect Spectrum: A Granular Look of Large-Scale Defect Datasets with Rich Semantics [ECCV 2024][data]
AD3: Introducing a score for Anomaly Detection Dataset Difficulty assessment using VIADUCT dataset [ECCV 2024]
Learning Diffusion Models for Multi-View Anomaly Detection [ECCV 2024]
MoEAD: A Parameter-efficient Model for Multi-class Anomaly Detection [ECCV 2024][code]
Unsupervised, Online and On-The-Fly Anomaly Detection For Non-Stationary Image Distributions [ECCV 2024][code]

ACM MM 2024

FiLo: Zero-Shot Anomaly Detection by Fine-Grained Description and High-Quality Localization [ACM MM 2024][code]
Dual-Modeling Decouple Distillation for Unsupervised Anomaly Detection [ACM MM 2024]
FOCT: Few-shot Industrial Anomaly Detection with Foreground-aware Online Conditional Transport [ACM MM 2024]
Towards High-resolution 3D Anomaly Detection via Group-Level Feature Contrastive Learning [ACM MM 2024][code]

ICASSP 2024

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]
Feature-Constrained and Attention-Conditioned Distillation Learning for Visual Anomaly Detection [ICASSP 2024]
CAGEN: Controllable Anomaly Generator using Diffusion Model [ICASSP 2024]
Mixed-Attention Auto Encoder for Multi-Class Industrial Anomaly Detection [ICASSP 2024]

CVPR 2024

Text-Guided Variational Image Generation for Industrial Anomaly Detection and Segmentation [CVPR 2024][code]
RealNet: A Feature Selection Network with Realistic Synthetic Anomaly for Anomaly Detection [CVPR 2024][code]
Toward Generalist Anomaly Detection via In-context Residual Learning with Few-shot Sample Prompts [CVPR 2024][code]
Multimodal Industrial Anomaly Detection by Crossmodal Feature Mapping [CVPR 2024]
Towards Scalable 3D Anomaly Detection and Localization: A Benchmark via 3D Anomaly Synthesis and A Self-Supervised Learning Network [CVPR 2024][code]
Real-IAD: A Real-World Multi-view Dataset for Benchmarking Versatile Industrial Anomaly Detection [CVPR 2024][code][data]
Long-Tailed Anomaly Detection with Learnable Class Names [CVPR 2024][data split]
PromptAD: Learning Prompts with only Normal Samples for Few-Shot Anomaly Detection [CVPR 2024][code]
Supervised Anomaly Detection for Complex Industrial Images [CVPR 2024][code]
Anomaly Heterogeneity Learning for Open-set Supervised Anomaly Detection [CVPR 2024][code]
Prompt-enhanced Multiple Instance Learning for Weakly Supervised Anomaly Detection [CVPR 2024][code]
Looking 3D: Anomaly Detection with 2D-3D Alignment [CVPR 2024][homepage][code]
CVPRW: VAND 2.0: Visual Anomaly and Novelty Detection - 2nd Edition [Challenge and Call for Papers]
Divide and Conquer: High-Resolution Industrial Anomaly Detection via Memory Efficient Tiled Ensemble [CVPR 24 Visual Anomaly Detection Workshop][homepage]

ICLR 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]

AAAI 2024

Rethinking Reverse Distillation for Multi-Modal Anomaly Detection [AAAI 2024]
Unsupervised Continual Anomaly Detection with Contrastively-learned Prompt [AAAI 2024][code]
Few Shot Part Segmentation Reveals Compositional Logic for Industrial Anomaly Detection [AAAI 2024][code]
DiAD: A Diffusion-based Framework for Multi-class Anomaly Detection [AAAI 2024][code]
Generating and Reweighting Dense Contrastive Patterns for Unsupervised Anomaly Detection [AAAI 2024]
AnomalyDiffusion: Few-Shot Anomaly Image Generation with Diffusion Model [AAAI 2024][code]
AnomalyGPT: Detecting Industrial Anomalies using Large Vision-Language Models [AAAI 2024][code][project page]
A Comprehensive Augmentation Framework for Anomaly Detection [AAAI 2024]

WACV 2024

ReConPatch: Contrastive Patch Representation Learning for Industrial Anomaly Detection [WACV 2024]
Learning Transferable Representations for Image Anomaly Localization Using Dense Pretraining [WACV 2024][code]
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies [WACV 2024]
Contextual Affinity Distillation for Image Anomaly Detection [WACV 2024]
Attention Modules Improve Image-Level Anomaly Detection for Industrial Inspection: A DifferNet Case Study [WACV 2024]
PromptAD: Zero-shot Anomaly Detection using Text Prompts [WACV 2024]
High-Fidelity Zero-Shot Texture Anomaly Localization Using Feature Correspondence Analysis [WACV 2024]
Cheating Depth: Enhancing 3D Surface Anomaly Detection via Depth Simulation [WACV 2024][code]

NeurIPS 2023

Real3D-AD: A Dataset of Point Cloud Anomaly Detection [NeurIPS 2023][code][中文]
PAD: A Dataset and Benchmark for Pose-agnostic Anomaly Detection [NeurIPS 2023][code]
Zero-Shot Anomaly Detection via Batch Normalization [NeurIPS 2023][code]
SANFlow: Semantic-Aware Normalizing Flow for Anomaly Detection and Localization [NeurIPS 2023]
Energy-Based Models for Anomaly Detection: A Manifold Diffusion Recovery Approach [NeurIPS 2023]
Hierarchical Vector Quantized Transformer for Multi-class Unsupervised Anomaly Detection [NeurIPS 2023][code]
ReContrast: Domain-Specific Anomaly Detection via Contrastive Reconstruction [NeurIPS 2023][code]

LLM related

Myriad: Large Multimodal Model by Applying Vision Experts for Industrial Anomaly Detection [2023][code]
AnomalyGPT: Detecting Industrial Anomalies using Large Vision-Language Models [AAAI 2024][code][project page]
The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision) [2023 Section 9.2]
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 [2023][code]
Customizing Visual-Language Foundation Models for Multi-modal Anomaly Detection and Reasoning [2024]
Do LLMs Understand Visual Anomalies? Uncovering LLM Capabilities in Zero-shot Anomaly Detection [2024]
LogiCode: an LLM-Driven Framework for Logical Anomaly Detection [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 [2024]

SAM segment anything

Segment Anything Is Not Always Perfect: An Investigation of SAM on Different Real-world Applications [2023 SAM tech report]
SAM Struggles in Concealed Scenes -- Empirical Study on "Segment Anything" [2023 SAM tech report]
Segment Any Anomaly without Training via Hybrid Prompt Regularization [2023] [code]
Application of Segment Anything Model for Civil Infrastructure Defect Assessment [2023 SAM tech report]
Segment Anything in Defect Detection [2023]
Unsupervised Continual Anomaly Detection with Contrastively-learned Prompt [AAAI 2024][code]
ClipSAM: CLIP and SAM Collaboration for Zero-Shot Anomaly Segmentation [2023]
A SAM-guided Two-stream Lightweight Model for Anomaly Detection [2024][code]

Others

Self-supervised Context Learning for Visual Inspection of Industrial Defects [2023][code]
CLIP-AD: A Language-Guided Staged Dual-Path Model for Zero-shot Anomaly Detection [2023]
Self-Tuning Self-Supervised Anomaly Detection [2023]
Model Selection of Anomaly Detectors in the Absence of Labeled Validation Data [2023]
A Discrepancy Aware Framework for Robust Anomaly Detection [2023][code]
The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision) [2023 Section 9.2]
Global Context Aggregation Network for Lightweight Saliency Detection of Surface Defects [2023]
Decision Fusion Network with Perception Fine-tuning for Defect Classification [2023]
FAIR: Frequency-aware Image Restoration for Industrial Visual Anomaly Detection [2023][code]
AnoVL: Adapting Vision-Language Models for Unified Zero-shot Anomaly Localization [2023][code]
End-to-End Augmentation Hyperparameter Tuning for Self-Supervised Anomaly Detection [2023]
CVPR 1st workshop on Vision-based InduStrial InspectiON [CVPR 2023 Workshop] [data link]
Multilevel Saliency-Guided Self-Supervised Learning for Image Anomaly Detection [2023]
How Low Can You Go? Surfacing Prototypical In-Distribution Samples for Unsupervised Anomaly Detection [Dataset Distillation][2023]
Exploring Plain ViT Reconstruction for Multi-class Unsupervised Anomaly Detection [2023]
AUPIMO: Redefining Visual Anomaly Detection Benchmarks with High Speed and Low Tolerance [2024]
Model Selection of Zero-shot Anomaly Detectors in the Absence of Labeled Validation Data [2024]
PUAD: Frustratingly Simple Method for Robust Anomaly Detection [2024]
COFT-AD: COntrastive Fine-Tuning for Few-Shot Anomaly Detection [TIP2024]
PointCore: Efficient Unsupervised Point Cloud Anomaly Detector Using Local-Global Features [2024]
Learning Unified Reference Representation for Unsupervised Multi-class Anomaly Detection [2024]
RAD: A Comprehensive Dataset for Benchmarking the Robustness of Image Anomaly Detection [CASE 2024][github page]
Towards Zero-shot Point Cloud Anomaly Detection: A Multi-View Projection Framework [2024][code]

Medical (related)

Towards Universal Unsupervised Anomaly Detection in Medical Imaging [2024]
MAEDiff: Masked Autoencoder-enhanced Diffusion Models for Unsupervised Anomaly Detection in Brain Images [2024]
BMAD: Benchmarks for Medical Anomaly Detection [2023]
Unsupervised Pathology Detection: A Deep Dive Into the State of the Art [2023]
Adapting Visual-Language Models for Generalizable Anomaly Detection in Medical Images [CVPR 2024]
Multi-Image Visual Question Answering for Unsupervised Anomaly Detection [2024]

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]
Exploring Plain ViT Reconstruction for Multi-class Unsupervised Anomaly Detection [2023][code]
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]

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]
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]

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]

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]

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]
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]

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]

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]
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]

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]

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]

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]
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]

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]

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]

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]
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]
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]
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]

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]
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]

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]

3.3 Anomaly Synthetic

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]

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]

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]
MulSen-AD: A Dataset and Benchmark for Multi-Sensor Anomaly Detection [2024][code]
PointAD: Comprehending 3D Anomalies from Points and Pixels for Zero-shot 3D Anomaly Detection [NeurIPS 2024][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]
An Incremental Unified Framework for Small Defect Inspection [2023][code]
Unsupervised Continual Anomaly Detection with Contrastively-learned Prompt [AAAI 2024][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]
Exploring Plain ViT Reconstruction for Multi-class Unsupervised Anomaly Detection [2023]
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]
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 [2024]
Prior Normality Prompt Transformer for Multi-class Industrial Image Anomaly Detection [TII 2024]
An Incremental Unified Framework for Small Defect Inspection [ECCV2024][code]
Learning Multi-view Anomaly Detection [2024]

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]
Unsupervised Component Segmentation for Logical Anomaly Detection [2024] [code]
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]

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]

Defect Classification

AnomalyNCD: Towards Novel Anomaly Class Discovery in Industrial Scenarios [2024][code coming soon]

4 Dataset

Dataset	Class	Normal	Abnormal	Total	Annotation level	Source	Time
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
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]
(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]
MulSen-AD: A Dataset and Benchmark for Multi-Sensor Anomaly Detection [2024][data]
CableInspect-AD: An Expert-Annotated Anomaly Detection Dataset [NeurIPS 2024][data]
RAD: A Dataset and Benchmark for Real-Life Anomaly Detection with Robotic Observations [2024][data]
AD3: Introducing a score for Anomaly Detection Dataset Difficulty assessment using VIADUCT dataset [ECCV 2024]
MMAD: The First-Ever Comprehensive Benchmark for Multimodal Large Language Models in Industrial Anomaly Detection [2024] [data]

BibTex Citation

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@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}
}