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https://github.com/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)。
https://github.com/M-3LAB/awesome-industrial-anomaly-detection
List: awesome-industrial-anomaly-detection
anomaly-detection anomaly-segmentation computer-vision dataset deep-learning defect-detection industrial-image
Last synced: about 2 months ago
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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)。
- Host: GitHub
- URL: https://github.com/M-3LAB/awesome-industrial-anomaly-detection
- Owner: M-3LAB
- Created: 2022-12-02T06:57:56.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-04-09T12:07:30.000Z (5 months ago)
- Last Synced: 2024-04-10T04:38:59.424Z (5 months ago)
- Topics: anomaly-detection, anomaly-segmentation, computer-vision, dataset, deep-learning, defect-detection, industrial-image
- Homepage:
- Size: 5.76 MB
- Stars: 913
- Watchers: 46
- Forks: 96
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-object-detection-datasets - M-3LAB/awesome-industrial-anomaly-detection - 3LAB/awesome-industrial-anomaly-detection?style=social"/> : Paper list and datasets for industrial image anomaly detection. (Summary)
- ultimate-awesome - 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)。. (Other Lists / PowerShell Lists)
README
# Awesome Industrial Anomaly Detection [](https://github.com/sindresorhus/awesome)
We discuss public datasets and related studies in detail. Welcome to read our paper and make comments.
[Deep Industrial Image Anomaly Detection: A Survey (Machine Intelligence Research)](https://link.springer.com/article/10.1007/s11633-023-1459-z)
[IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturing [TCYB 2024]](https://arxiv.org/abs/2301.13359)[[code]](https://github.com/M-3LAB/open-iad)[[中文]](https://blog.csdn.net/m0_63828250/article/details/136891730)
We will keep focusing on this field and updating relevant information.
Keywords: anomaly detection, anomaly segmentation, industrial image, defect detection
[[Main Page]](https://github.com/M-3LAB) [[Survey]](https://github.com/M-3LAB/awesome-industrial-anomaly-detection) [[Benchmark]](https://github.com/M-3LAB/open-iad) [[Result]](https://github.com/M-3LAB/IM-IAD)
# SOTA methods with code
| Title | Venue | Date | Code | topic |
|:--------|:--------:|:--------:|:--------:|:--------:|
| 
[**Anomaly Detection via Reverse Distillation from One-Class Embedding**](https://openaccess.thecvf.com/content/CVPR2022/html/Deng_Anomaly_Detection_via_Reverse_Distillation_From_One-Class_Embedding_CVPR_2022_paper.html)
| CVPR | 2022 | [Github](https://github.com/hq-deng/RD4AD) | Teacher-Student |
| 
[**Revisiting Reverse Distillation for Anomaly Detection**](https://openaccess.thecvf.com/content/CVPR2023/html/Tien_Revisiting_Reverse_Distillation_for_Anomaly_Detection_CVPR_2023_paper.html)
| CVPR | 2023 | [Github](https://github.com/tientrandinh/Revisiting-Reverse-Distillation) | Teacher-Student |
| 
[**SimpleNet: A Simple Network for Image Anomaly Detection and Localization**](https://openaccess.thecvf.com/content/CVPR2023/html/Liu_SimpleNet_A_Simple_Network_for_Image_Anomaly_Detection_and_Localization_CVPR_2023_paper.html)
| CVPR | 2023 | [Github](https://github.com/DonaldRR/SimpleNet) | One-Class-Classification |
| 
[**Real-time unsupervised anomaly detection with localization via conditional normalizing flows**](https://openaccess.thecvf.com/content/WACV2022/html/Gudovskiy_CFLOW-AD_Real-Time_Unsupervised_Anomaly_Detection_With_Localization_via_Conditional_Normalizing_WACV_2022_paper.html)
| WACV | 2022 | [Github](https://github.com/gudovskiy/cflow-ad) | Distribution Map |
| 
[**PyramidFlow: High-Resolution Defect Contrastive Localization using Pyramid Normalizing Flow**](https://openaccess.thecvf.com/content/CVPR2023/html/Lei_PyramidFlow_High-Resolution_Defect_Contrastive_Localization_Using_Pyramid_Normalizing_Flow_CVPR_2023_paper.html)
| CVPR | 2023 | [Github](https://github.com/gasharper/PyramidFlow) | Distribution Map |
| 
[**Towards total recall in industrial anomaly detection**](https://openaccess.thecvf.com/content/CVPR2022/html/Roth_Towards_Total_Recall_in_Industrial_Anomaly_Detection_CVPR_2022_paper.html)
| CVPR | 2022 | [Github](https://github.com/amazon-science/patchcore-inspection) | Memory-bank |
| 
[**PNI: Industrial Anomaly Detection using Position and Neighborhood Information**](https://openaccess.thecvf.com/content/ICCV2023/html/Bae_PNI__Industrial_Anomaly_Detection_using_Position_and_Neighborhood_Information_ICCV_2023_paper.html)
| ICCV | 2023 | [Github](https://github.com/wogur110/PNI_Anomaly_Detection) | Memory-bank |
| 
[**Draem-a discriminatively trained reconstruction embedding for surface anomaly detection**](https://openaccess.thecvf.com/content/ICCV2021/html/Zavrtanik_DRAEM_-_A_Discriminatively_Trained_Reconstruction_Embedding_for_Surface_Anomaly_ICCV_2021_paper.html)
| ICCV | 2021 | [Github](https://github.com/vitjanz/draem) | Reconstruction-based |
| 
[**DSR: A dual subspace re-projection network for surface anomaly detection**](https://link.springer.com/chapter/10.1007/978-3-031-19821-2_31)
| ECCV | 2022 | [Github](https://github.com/VitjanZ/DSR_anomaly_detection) | Reconstruction-based |
| 
[**Omni-frequency Channel-selection Representations for Unsupervised Anomaly Detection**](https://ieeexplore.ieee.org/abstract/document/10192551/)
| TIP | 2023 | [Github](https://github.com/zhangzjn/ocr-gan) | Reconstruction-based |
| 
[**RealNet: A Feature Selection Network with Realistic Synthetic Anomaly for Anomaly Detection**](https://arxiv.org/abs/2403.05897)
| CVPR | 2024 | [Github](https://github.com/cnulab/RealNet) | Reconstruction-based |
| 
[**Registration based few-shot anomaly detection**](https://link.springer.com/chapter/10.1007/978-3-031-20053-3_18)
| ECCV | 2022 | [Github](https://github.com/MediaBrain-SJTU/RegAD) | Few Shot |
| 
[**AnomalyGPT: Detecting Industrial Anomalies using Large Vision-Language Models**](https://arxiv.org/abs/2308.15366)
| AAAI | 2024 | [Github](https://github.com/CASIA-IVA-Lab/AnomalyGPT) | Few Shot |
| 
[**Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detection**](https://openaccess.thecvf.com/content/CVPR2022/html/Ding_Catching_Both_Gray_and_Black_Swans_Open-Set_Supervised_Anomaly_Detection_CVPR_2022_paper.html)
| CVPR | 2022 | [Github](https://github.com/Choubo/DRA) | Few abnormal samples |
| 
[**Explicit Boundary Guided Semi-Push-Pull Contrastive Learning for Supervised Anomaly Detection**](https://openaccess.thecvf.com/content/CVPR2023/html/Yao_Explicit_Boundary_Guided_Semi-Push-Pull_Contrastive_Learning_for_Supervised_Anomaly_Detection_CVPR_2023_paper.html)
| CVPR | 2023 | [Github](https://github.com/xcyao00/BGAD) | Few abnormal samples |
| 
[**Deep one-class classification via interpolated gaussian descriptor**](https://ojs.aaai.org/index.php/AAAI/article/view/19915)
| AAAI | 2022 | [Github](https://github.com/tianyu0207/IGD) | Noisy AD |
| 
[**SoftPatch: Unsupervised Anomaly Detection with Noisy Data**](https://proceedings.neurips.cc/paper_files/paper/2022/hash/637a456d89289769ac1ab29617ef7213-Abstract-Conference.html)
| NeurIPS | 2022 | [Github](https://github.com/TencentYoutuResearch/AnomalyDetection-SoftPatch) | Noisy AD |
| 
[**Inter-Realization Channels: Unsupervised Anomaly Detection Beyond One-Class Classification**](https://openaccess.thecvf.com/content/ICCV2023/html/McIntosh_Inter-Realization_Channels_Unsupervised_Anomaly_Detection_Beyond_One-Class_Classification_ICCV_2023_paper.html)
| ICCV | 2023 | [Github](https://github.com/DeclanMcIntosh/InReaCh) | Noisy AD |
| 
[**Unsupervised Continual Anomaly Detection with Contrastively-learned Prompt**](https://ojs.aaai.org/index.php/AAAI/article/view/28153)
| AAAI | 2024 | [Github](https://github.com/shirowalker/UCAD) | Continual AD |
| 
[**A Unified Model for Multi-class Anomaly Detection**](https://proceedings.neurips.cc/paper_files/paper/2022/hash/1d774c112926348c3e25ea47d87c835b-Abstract-Conference.html)
| NeurIPS | 2022 | [Github](https://github.com/zhiyuanyou/UniAD) | Multi-class unified |
| 
[**Hierarchical Vector Quantized Transformer for Multi-class Unsupervised Anomaly Detection**](https://openreview.net/pdf?id=clJTNssgn6)
| NeurIPS | 2023 | [Github](https://github.com/RuiyingLu/HVQ-Trans) | Multi-class unified |
| 
[**Multimodal Industrial Anomaly Detection via Hybrid Fusion**](https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Multimodal_Industrial_Anomaly_Detection_via_Hybrid_Fusion_CVPR_2023_paper.html)
| CVPR | 2023 | [Github](https://github.com/nomewang/M3DM) | RGBD |
| 
[**Real3D-AD: A Dataset of Point Cloud Anomaly Detection**](https://openreview.net/pdf?id=zGthDp4yYe)
| NeurIPS | 2023 | [Github](https://github.com/M-3LAB/Real3D-AD) | Point Cloud |
| 
[**Anomalib: A Deep Learning Library for Anomaly Detection**](https://arxiv.org/abs/2307.12540)
| ICIP | 2022 | [Github](https://github.com/openvinotoolkit/anomalib) | Benchmark |
| 
[**IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturing**](https://arxiv.org/abs/2301.13359)
| TCYB | 2024 | [Github](https://github.com/M-3LAB/open-iad) | Benchmark |
| 
[**ADer: A Comprehensive Benchmark for Multi-class Visual Anomaly Detection**](http://arxiv.org/pdf/2406.03262v1)
| arxiv | 2024 | [Github](https://github.com/zhangzjn/ader) | Benchmark |
| 
[**AnoVL: Adapting Vision-Language Models for Unified Zero-shot Anomaly Localization**](https://arxiv.org/abs/2308.15939)
| arxiv | 2023 | [Github](https://github.com/hq-deng/AnoVL) | Zero Shot |
| 
[**Segment Any Anomaly without Training via Hybrid Prompt Regularization**](https://arxiv.org/abs/2305.10724)
| arxiv | 2023 | [Github](https://github.com/caoyunkang/GroundedSAM-zero-shot-anomaly-detection) | Zero Shot |
| 
[**PSAD: Few Shot Part Segmentation Reveals Compositional Logic for Industrial Anomaly Detection**](https://ojs.aaai.org/index.php/AAAI/article/view/28703)
| AAAI | 2024 | [Github](https://github.com/oopil/PSAD_logical_anomaly_detection) | Logical/Few Shot |
| 
[**UniFormaly: Towards Task-Agnostic Unified Framework for Visual Anomaly Detection**](https://arxiv.org/abs/2307.12540)
| arxiv | 2023 | [Github](https://github.com/YoojLee/Uniformaly) | Multi-class unified |
# Recent research
## ECCV 2024
+ R3D-AD: Reconstruction via Diffusion for 3D Anomaly Detection [[ECCV 2024]](https://arxiv.org/abs/2407.10862)[[homepage]](https://zhouzheyuan.github.io/r3d-ad)
+ An Incremental Unified Framework for Small Defect Inspection [[ECCV2024]](https://arxiv.org/abs/2312.08917v2)[[code]](https://github.com/jqtangust/IUF)
+ Learning Unified Reference Representation for Unsupervised Multi-class Anomaly Detection [[ECCV 2024]](https://arxiv.org/abs/2403.11561)[[code]](https://github.com/hlr7999/RLR)
+ Self-supervised Feature Adaptation for 3D Industrial Anomaly Detection [[ECCV 2024]](https://arxiv.org/abs/2401.03145)
+ Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt [[ECCV 2024]](https://csgaobb.github.io/Pub_files/ECCV2024_OneNIP_CR_Full_0725_Mobile.pdf)[[code]](https://github.com/gaobb/OneNIP)
+ Few-Shot Anomaly-Driven Generation for Anomaly Classification and Segmentation [[ECCV 2024]](https://csgaobb.github.io/Pub_files/ECCV2024_AnoGen_CR_0730_Mobile.pdf)[[code]](https://github.com/gaobb/AnoGen)
+ AdaCLIP: Adapting CLIP with Hybrid Learnable Prompts for Zero-Shot Anomaly Detection [[ECCV 2024]](https://arxiv.org/abs/2407.15795)[[code]](https://github.com/caoyunkang/AdaCLIP)
+ GLAD: Towards Better Reconstruction with Global and Local Adaptive Diffusion Models for Unsupervised Anomaly Detection [[ECCV 2024]](https://arxiv.org/abs/2406.07487)[[code]](https://github.com/hyao1/GLAD)
+ GeneralAD: Anomaly Detection Across Domains by Attending to Distorted Features [[ECCV 2024]](https://arxiv.org/abs/2407.12427)[[code]](https://github.com/LucStrater/GeneralAD)
+ VCP-CLIP: A visual context prompting model for zero-shot anomaly segmentation [[ECCV 2024]](https://arxiv.org/abs/2407.12276)[[code]](https://github.com/xiaozhen228/VCP-CLIP)
+ A Unified Anomaly Synthesis Strategy with Gradient Ascent for Industrial Anomaly Detection and Localization [[ECCV 2024]](https://arxiv.org/abs/2407.09359)[[code]](https://github.com/cqylunlun/GLASS)
+ Hierarchical Gaussian Mixture Normalizing Flow Modeling for Unified Anomaly Detection [[ECCV 2024]](https://arxiv.org/abs/2403.13349)[[code]](https://github.com/xcyao00/HGAD)
+ TransFusion -- A Transparency-Based Diffusion Model for Anomaly Detection [[ECCV 2024]](https://arxiv.org/abs/2311.09999)[[code]](https://github.com/MaticFuc/ECCV_TransFusion)
## ICASSP 2024
+ Implicit Foreground-Guided Network for Anomaly Detection and Localization [[ICASSP 2024]](https://ieeexplore.ieee.org/abstract/document/10446952)
+ Neural Network Training Strategy To Enhance Anomaly Detection Performance: A Perspective On Reconstruction Loss Amplification [[ICASSP 2024]](https://ieeexplore.ieee.org/document/10446942)
+ Patch-Wise Augmentation for Anomaly Detection and Localization [[ICASSP 2024]](https://ieeexplore.ieee.org/document/10446994)
+ A Reconstruction-Based Feature Adaptation for Anomaly Detection with Self-Supervised Multi-Scale Aggregation [[ICASSP 2024]](https://ieeexplore.ieee.org/document/10446766)
+ Feature-Constrained and Attention-Conditioned Distillation Learning for Visual Anomaly Detection [[ICASSP 2024]](https://ieeexplore.ieee.org/document/10448432)
+ CAGEN: Controllable Anomaly Generator using Diffusion Model [[ICASSP 2024]](https://ieeexplore.ieee.org/document/10447663)
+ Mixed-Attention Auto Encoder for Multi-Class Industrial Anomaly Detection [[ICASSP 2024]](https://ieeexplore.ieee.org/document/10446794)
## CVPR 2024
+ Text-Guided Variational Image Generation for Industrial Anomaly Detection and Segmentation [[CVPR 2024]](https://arxiv.org/abs/2403.06247)
+ RealNet: A Feature Selection Network with Realistic Synthetic Anomaly for Anomaly Detection [[CVPR 2024]](https://arxiv.org/abs/2403.05897)[[code]](https://github.com/cnulab/RealNet)
+ Toward Generalist Anomaly Detection via In-context Residual Learning with Few-shot Sample Prompts [[CVPR 2024]](https://arxiv.org/abs/2403.06495)[[code]](https://github.com/mala-lab/InCTRL)
+ Multimodal Industrial Anomaly Detection by Crossmodal Feature Mapping [[CVPR 2024]](https://arxiv.org/abs/2312.04521)
+ Towards Scalable 3D Anomaly Detection and Localization: A Benchmark via 3D Anomaly Synthesis and A Self-Supervised Learning Network [[CVPR 2024]](https://arxiv.org/abs/2311.14897)[[code]](https://github.com/Chopper-233/Anomaly-ShapeNet)
+ Real-IAD: A Real-World Multi-view Dataset for Benchmarking Versatile Industrial Anomaly Detection [[CVPR 2024]](https://arxiv.org/abs/2403.12580)[[code]](https://github.com/TencentYoutuResearch/AnomalyDetection_Real-IAD)[[data]](https://realiad4ad.github.io/Real-IAD/)
+ Long-Tailed Anomaly Detection with Learnable Class Names [[CVPR 2024]](https://arxiv.org/abs/2403.20236)[[data split]](https://zenodo.org/records/10854201)
+ PromptAD: Learning Prompts with only Normal Samples for Few-Shot Anomaly Detection [[CVPR 2024]](https://arxiv.org/abs/2404.05231)[[code]](https://github.com/FuNz-0/PromptAD)
+ Supervised Anomaly Detection for Complex Industrial Images [[CVPR 2024 comming]]()
+ CVPRW: VAND 2.0: Visual Anomaly and Novelty Detection - 2nd Edition [[Challenge and Call for Papers]](https://sites.google.com/view/vand-2-0-cvpr-2024/home)
+ Anomaly Heterogeneity Learning for Open-set Supervised Anomaly Detection [[CVPR 2024]](https://arxiv.org/abs/2310.12790)[[code]](https://github.com/mala-lab/AHL)
## ICLR 2024
+ AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detection [[ICLR 2024]](https://openreview.net/forum?id=buC4E91xZE)[[code]](https://github.com/zqhang/AnomalyCLIP)
+ MuSc: Zero-Shot Industrial Anomaly Classification and Segmentation with Mutual Scoring of the Unlabeled Images[[ICLR 2024]](https://openreview.net/forum?id=AHgc5SMdtd)[[code]](https://github.com/xrli-U/MuSc)
## AAAI 2024
+ Rethinking Reverse Distillation for Multi-Modal Anomaly Detection [[AAAI 2024]](https://ojs.aaai.org/index.php/AAAI/article/view/28687)
+ Unsupervised Continual Anomaly Detection with Contrastively-learned Prompt [[AAAI 2024]](https://ojs.aaai.org/index.php/AAAI/article/view/28153)[[code]](https://github.com/shirowalker/UCAD)
+ Few Shot Part Segmentation Reveals Compositional Logic for Industrial Anomaly Detection [[AAAI 2024]](https://ojs.aaai.org/index.php/AAAI/article/view/28703)[[code]](https://github.com/oopil/PSAD_logical_anomaly_detection)
+ DiAD: A Diffusion-based Framework for Multi-class Anomaly Detection [[AAAI 2024]](https://ojs.aaai.org/index.php/AAAI/article/view/28690)[[code]](https://lewandofskee.github.io/projects/diad)
+ Generating and Reweighting Dense Contrastive Patterns for Unsupervised Anomaly Detection [[AAAI 2024]](https://ojs.aaai.org/index.php/AAAI/article/view/27910)
+ AnomalyDiffusion: Few-Shot Anomaly Image Generation with Diffusion Model [[AAAI 2024]](https://ojs.aaai.org/index.php/AAAI/article/view/28696)[[code]](https://github.com/sjtuplayer/anomalydiffusion)
+ AnomalyGPT: Detecting Industrial Anomalies using Large Vision-Language Models [[AAAI 2024]](https://ojs.aaai.org/index.php/AAAI/article/view/27963)[[code]](https://github.com/CASIA-IVA-Lab/AnomalyGPT)[[project page]](https://anomalygpt.github.io/)
+ A Comprehensive Augmentation Framework for Anomaly Detection [[AAAI 2024]](https://ojs.aaai.org/index.php/AAAI/article/view/28720)
## WACV 2024
+ ReConPatch: Contrastive Patch Representation Learning for Industrial Anomaly Detection [[WACV 2024]](https://openaccess.thecvf.com/content/WACV2024/papers/Hyun_ReConPatch_Contrastive_Patch_Representation_Learning_for_Industrial_Anomaly_Detection_WACV_2024_paper.pdf)
+ Learning Transferable Representations for Image Anomaly Localization Using Dense Pretraining [[WACV 2024]](https://openaccess.thecvf.com/content/WACV2024/papers/He_Learning_Transferable_Representations_for_Image_Anomaly_Localization_Using_Dense_Pretraining_WACV_2024_paper.pdf)[[code]](https://github.com/terrlo/DS2)
+ EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies [[WACV 2024]](https://openaccess.thecvf.com/content/WACV2024/papers/Batzner_EfficientAD_Accurate_Visual_Anomaly_Detection_at_Millisecond-Level_Latencies_WACV_2024_paper.pdf)
+ Contextual Affinity Distillation for Image Anomaly Detection [[WACV 2024]](https://openaccess.thecvf.com/content/WACV2024/papers/Zhang_Contextual_Affinity_Distillation_for_Image_Anomaly_Detection_WACV_2024_paper.pdf)
+ Attention Modules Improve Image-Level Anomaly Detection for Industrial Inspection: A DifferNet Case Study [[WACV 2024]](https://openaccess.thecvf.com/content/WACV2024/papers/Vieira_e_Silva_Attention_Modules_Improve_Image-Level_Anomaly_Detection_for_Industrial_Inspection_A_WACV_2024_paper.pdf)
+ PromptAD: Zero-shot Anomaly Detection using Text Prompts [[WACV 2024]](https://openaccess.thecvf.com/content/WACV2024/papers/Li_PromptAD_Zero-Shot_Anomaly_Detection_Using_Text_Prompts_WACV_2024_paper.pdf)
+ High-Fidelity Zero-Shot Texture Anomaly Localization Using Feature Correspondence Analysis [[WACV 2024]](https://openaccess.thecvf.com/content/WACV2024/html/Ardelean_High-Fidelity_Zero-Shot_Texture_Anomaly_Localization_Using_Feature_Correspondence_Analysis_WACV_2024_paper.html)
+ Cheating Depth: Enhancing 3D Surface Anomaly Detection via Depth Simulation [[WACV 2024]](https://openaccess.thecvf.com/content/WACV2024/papers/Zavrtanik_Cheating_Depth_Enhancing_3D_Surface_Anomaly_Detection_via_Depth_Simulation_WACV_2024_paper.pdf)[[code]](https://github.com/VitjanZ/3DSR)
## NeurIPS 2023
+ Real3D-AD: A Dataset of Point Cloud Anomaly Detection [[NeurIPS 2023]](https://openreview.net/pdf?id=zGthDp4yYe)[[code]](https://github.com/M-3LAB/Real3D-AD)[[中文]](https://blog.csdn.net/m0_63828250/article/details/136667168)
+ PAD: A Dataset and Benchmark for Pose-agnostic Anomaly Detection [[NeurIPS 2023]](https://openreview.net/pdf?id=kxFKgqwFNk)[[code]](https://github.com/EricLee0224/PAD)
+ Zero-Shot Anomaly Detection via Batch Normalization [[NeurIPS 2023]](https://openreview.net/pdf?id=d1wjMBYbP1)[[code]](https://github.com/aodongli/zero-shot-ad-via-batch-norm)
+ SANFlow: Semantic-Aware Normalizing Flow for Anomaly Detection and Localization [[NeurIPS 2023]](https://openreview.net/pdf?id=BqZ70BEtuW)
+ Energy-Based Models for Anomaly Detection: A Manifold Diffusion Recovery Approach [[NeurIPS 2023]](https://openreview.net/pdf?id=4nSDDokpfK)
+ Hierarchical Vector Quantized Transformer for Multi-class Unsupervised Anomaly Detection [[NeurIPS 2023]](https://openreview.net/pdf?id=clJTNssgn6)[[code]](https://github.com/RuiyingLu/HVQ-Trans)
+ ReContrast: Domain-Specific Anomaly Detection via Contrastive Reconstruction [[NeurIPS 2023]](https://openreview.net/pdf?id=KYxD9YCQBH)[[code]](https://github.com/guojiajeremy/ReContrast)
## ICML 2023
+ Shape-Guided Dual-Memory Learning for 3D Anomaly Detection [[ICML 2023]](https://openreview.net/forum?id=IkSGn9fcPz)
+ Fascinating Supervisory Signals and Where to Find Them: Deep Anomaly Detection with Scale Learning [[ICML 2023]](https://openreview.net/forum?id=V6PNBRWRil)
## ACM MM 2023
+ EasyNet: An Easy Network for 3D Industrial Anomaly Detection [[ACM MM 2023]](https://arxiv.org/abs/2307.13925)
## ICCV 2023
+ Remembering Normality: Memory-guided Knowledge Distillation for Unsupervised Anomaly Detection [[ICCV 2023]](https://openaccess.thecvf.com/content/ICCV2023/papers/Gu_Remembering_Normality_Memory-guided_Knowledge_Distillation_for_Unsupervised_Anomaly_Detection_ICCV_2023_paper.pdf)
+ Unsupervised Surface Anomaly Detection with Diffusion Probabilistic Model [[ICCV 2023]](https://openaccess.thecvf.com/content/ICCV2023/papers/Zhang_Unsupervised_Surface_Anomaly_Detection_with_Diffusion_Probabilistic_Model_ICCV_2023_paper.pdf)
+ PNI: Industrial Anomaly Detection using Position and Neighborhood Information [[ICCV 2023]](https://openaccess.thecvf.com/content/ICCV2023/papers/Bae_PNI__Industrial_Anomaly_Detection_using_Position_and_Neighborhood_Information_ICCV_2023_paper.pdf)[[code]](https://github.com/wogur110/PNI_Anomaly_Detection)
+ Anomaly Detection using Score-based Perturbation Resilience [[ICCV 2023]](https://openaccess.thecvf.com/content/ICCV2023/papers/Shin_Anomaly_Detection_using_Score-based_Perturbation_Resilience_ICCV_2023_paper.pdf)
+ Template-guided Hierarchical Feature Restoration for Anomaly Detection [[ICCV 2023]](https://openaccess.thecvf.com/content/ICCV2023/papers/Guo_Template-guided_Hierarchical_Feature_Restoration_for_Anomaly_Detection_ICCV_2023_paper.pdf)
+ Focus the Discrepancy: Intra- and Inter-Correlation Learning for Image Anomaly Detection [[ICCV 2023]](https://openaccess.thecvf.com/content/ICCV2023/papers/Yao_Focus_the_Discrepancy_Intra-_and_Inter-Correlation_Learning_for_Image_Anomaly_ICCV_2023_paper.pdf)[[code]](https://github.com/xcyao00/FOD)
+ Anomaly Detection under Distribution Shift [[ICCV 2023]](https://openaccess.thecvf.com/content/ICCV2023/papers/Cao_Anomaly_Detection_Under_Distribution_Shift_ICCV_2023_paper.pdf)[[code]](https://github.com/mala-lab/ADShift)
+ FastRecon: Few-shot Industrial Anomaly Detection via Fast Feature Reconstruction [[ICCV 2023]](https://openaccess.thecvf.com/content/ICCV2023/papers/Fang_FastRecon_Few-shot_Industrial_Anomaly_Detection_via_Fast_Feature_Reconstruction_ICCV_2023_paper.pdf)[[code comming soon]](https://github.com/FzJun26th/FastRecon)
+ Inter-Realization Channels: Unsupervised Anomaly Detection Beyond One-Class Classification [[ICCV 2023]](https://openaccess.thecvf.com/content/ICCV2023/papers/McIntosh_Inter-Realization_Channels_Unsupervised_Anomaly_Detection_Beyond_One-Class_Classification_ICCV_2023_paper.pdf)[[code]](https://github.com/DeclanMcIntosh/InReaCh)
+ Removing Anomalies as Noises for Industrial Defect Localization [[ICCV 2023]](https://openaccess.thecvf.com/content/ICCV2023/papers/Lu_Removing_Anomalies_as_Noises_for_Industrial_Defect_Localization_ICCV_2023_paper.pdf)
## LLM related
+ Myriad: Large Multimodal Model by Applying Vision Experts for Industrial Anomaly Detection [[2023]](https://arxiv.org/abs/2310.19070)[[code]](https://github.com/tzjtatata/Myriad)
+ AnomalyGPT: Detecting Industrial Anomalies using Large Vision-Language Models [[AAAI 2024]](https://arxiv.org/abs/2308.15366)[[code]](https://github.com/CASIA-IVA-Lab/AnomalyGPT)[[project page]](https://anomalygpt.github.io/)
+ The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision) [[2023 Section 9.2]](https://arxiv.org/abs/2309.17421)
+ Towards Generic Anomaly Detection and Understanding: Large-scale Visual-linguistic Model (GPT-4V) Takes the Lead [[2023]](https://arxiv.org/abs/2311.02782)[[code]](https://github.com/caoyunkang/GPT4V-for-Generic-Anomaly-Detection)
+ Exploring Grounding Potential of VQA-oriented GPT-4V for Zero-shot Anomaly Detection [[2023]](https://arxiv.org/abs/2311.02612)[[code]](https://github.com/zhangzjn/GPT-4V-AD)
+ Customizing Visual-Language Foundation Models for Multi-modal Anomaly Detection and Reasoning [[2024]](https://arxiv.org/abs/2403.11083)
+ Do LLMs Understand Visual Anomalies? Uncovering LLM Capabilities in Zero-shot Anomaly Detection [[2024]](https://arxiv.org/abs/2404.09654)
+ LogiCode: an LLM-Driven Framework for Logical Anomaly Detection [[2024]](https://arxiv.org/pdf/2406.04687）
## CVPR 2023
+ CVPR 2023 Tutorial on "Recent Advances in Anomaly Detection" [[CVPR Workshop 2023(mainly on video anomaly detection)]](https://sites.google.com/view/cvpr2023-tutorial-on-ad/)[[video]](https://www.youtube.com/watch?v=dXxrzWeybBo&feature=youtu.be)
+ Workshop on Vision-Based Industrial Inspection [[CVPR Workshop paper list 2023]](https://openaccess.thecvf.com/CVPR2023_workshops/VISION)
+ Visual Anomaly and Novelty Detection [[CVPR Workshop paper list 2023]](https://openaccess.thecvf.com/CVPR2023_workshops/VAND)
+ Revisiting Reverse Distillation for Anomaly Detection [[CVPR 2023]](https://openaccess.thecvf.com/content/CVPR2023/papers/Tien_Revisiting_Reverse_Distillation_for_Anomaly_Detection_CVPR_2023_paper.pdf) [[code]](https://github.com/tientrandinh/Revisiting-Reverse-Distillation)
+ OmniAL A unifiled CNN framework for unsupervised anomaly localization [[CVPR 2023]](https://openaccess.thecvf.com/content/CVPR2023/papers/Zhao_OmniAL_A_Unified_CNN_Framework_for_Unsupervised_Anomaly_Localization_CVPR_2023_paper.pdf)
+ Explicit Boundary Guided Semi-Push-Pull Contrastive Learning for Supervised Anomaly Detection [[CVPR 2023]](https://arxiv.org/abs/2207.01463)[[code]](https://github.com/xcyao00/BGAD)
+ DeSTSeg: Segmentation Guided Denoising Student-Teacher for Anomaly Detection [[CVPR 2023]](https://arxiv.org/abs/2211.11317)[[code]](https://github.com/apple/ml-destseg)
+ Diversity-Measurable Anomaly Detection [[CVPR 2023]](https://arxiv.org/abs/2303.05047)
+ WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation [[CVPR 2023]](https://arxiv.org/abs/2303.14814)
+ SimpleNet: A Simple Network for Image Anomaly Detection and Localization [[CVPR 2023]](https://arxiv.org/abs/2303.15140)[[code]](https://github.com/DonaldRR/SimpleNet)
+ PyramidFlow: High-Resolution Defect Contrastive Localization using Pyramid Normalizing Flow [[CVPR 2023]](https://arxiv.org/abs/2303.02595)[[code]](https://github.com/gasharper/PyramidFlow)
+ Multimodal Industrial Anomaly Detection via Hybrid Fusion [[CVPR 2023]](https://arxiv.org/abs/2303.00601)[[code]](https://github.com/nomewang/M3DM)
+ Prototypical Residual Networks for Anomaly Detection and Localization [[CVPR 2023]](https://arxiv.org/abs/2212.02031)[[code]](https://github.com/xcyao00/PRNet)
+ SQUID: Deep Feature In-Painting for Unsupervised Anomaly Detection [[CVPR 2023]](https://arxiv.org/abs/2111.13495)
+ 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 [[CVPR 2023 VAND Workshop Challenge]](https://arxiv.org/abs/2305.17382)
## SAM segment anything
+ Segment Anything Is Not Always Perfect: An Investigation of SAM on Different Real-world Applications [[2023 SAM tech report]](https://arxiv.org/abs/2304.05750)
+ SAM Struggles in Concealed Scenes -- Empirical Study on "Segment Anything" [[2023 SAM tech report]](https://arxiv.org/abs/2304.06022)
+ Segment Any Anomaly without Training via Hybrid Prompt Regularization [[2023]](https://arxiv.org/abs/2305.10724) [[code]](https://github.com/caoyunkang/GroundedSAM-zero-shot-anomaly-detection)
+ Application of Segment Anything Model for Civil Infrastructure Defect Assessment [[2023 SAM tech report]](https://arxiv.org/abs/2304.12600)
+ Segment Anything in Defect Detection [[2023]](https://arxiv.org/abs/2311.10245)
+ Unsupervised Continual Anomaly Detection with Contrastively-learned Prompt [[AAAI 2024]](https://ojs.aaai.org/index.php/AAAI/article/view/28153)[[code]](https://github.com/shirowalker/UCAD)
+ ClipSAM: CLIP and SAM Collaboration for Zero-Shot Anomaly Segmentation [[2023]](https://arxiv.org/pdf/2401.12665)
+ A SAM-guided Two-stream Lightweight Model for Anomaly Detection [[2024]](https://arxiv.org/abs/2402.19145)
## ICLR 2023
+ Pushing the Limits of Fewshot Anomaly Detection in Industry Vision: Graphcore [[ICLR 2023]](https://openreview.net/pdf?id=xzmqxHdZAwO)
+ RGI: robust GAN-inversion for mask-free image inpainting and unsupervised pixel-wise anomaly detection [[ICLR 2023]](https://openreview.net/pdf?id=1UbNwQC89a)
## Others
+ Self-supervised Context Learning for Visual Inspection of Industrial Defects [[2023]](https://arxiv.org/abs/2311.06504)[[code]](https://github.com/wangpeng000/VisualInspection)
+ CLIP-AD: A Language-Guided Staged Dual-Path Model for Zero-shot Anomaly Detection [[2023]](https://arxiv.org/abs/2311.00453)
+ Self-Tuning Self-Supervised Anomaly Detection [[2023]](https://openreview.net/forum?id=saj54kqrBj)
+ Defect Spectrum: A Granular Look of Large-Scale Defect Datasets with Rich Semantics [[2023]](https://openreview.net/forum?id=RLhS1TrjK3)[[data]](https://defect-spectrum-authors.github.io/defect-spectrum/)
+ Model Selection of Anomaly Detectors in the Absence of Labeled Validation Data [[2023]](https://arxiv.org/abs/2310.10461)
+ A Discrepancy Aware Framework for Robust Anomaly Detection [[2023]](https://arxiv.org/abs/2310.07585)[[code]](https://github.com/caiyuxuan1120/DAF)
+ The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision) [[2023 Section 9.2]](https://arxiv.org/abs/2309.17421)
+ Global Context Aggregation Network for Lightweight Saliency Detection of Surface Defects [[2023]](https://arxiv.org/abs/2309.12641)
+ Decision Fusion Network with Perception Fine-tuning for Defect Classification [[2023]](https://arxiv.org/abs/2309.12630)
+ FAIR: Frequency-aware Image Restoration for Industrial Visual Anomaly Detection [[2023]](https://arxiv.org/abs/2309.07068)[[code]](https://github.com/liutongkun/FAIR)
+ AnoVL: Adapting Vision-Language Models for Unified Zero-shot Anomaly Localization [[2023]](https://arxiv.org/abs/2308.15939)[[code]](https://github.com/hq-deng/AnoVL)
+ End-to-End Augmentation Hyperparameter Tuning for Self-Supervised Anomaly Detection [[2023]](https://arxiv.org/abs/2306.12033)
+ CVPR 1st workshop on Vision-based InduStrial InspectiON [[CVPR 2023 Workshop]](https://vision-based-industrial-inspection.github.io/cvpr-2023/) [[data link]](https://drive.google.com/drive/folders/1TVp_UXJuXudqhC2L3ZKyIDcmQ_2O3JVi)
+ Multilevel Saliency-Guided Self-Supervised Learning for Image Anomaly Detection [[2023]](http://arxiv.org/pdf/2311.18332v1)
+ How Low Can You Go? Surfacing Prototypical In-Distribution Samples for Unsupervised Anomaly Detection [Dataset Distillation][[2023]](http://arxiv.org/pdf/2312.03804v1)
+ Exploring Plain ViT Reconstruction for Multi-class Unsupervised Anomaly Detection [[2023]](https://arxiv.org/abs/2312.07495)
+ AUPIMO: Redefining Visual Anomaly Detection Benchmarks with High Speed and Low Tolerance [[2024]](https://arxiv.org/abs/2401.01984)
+ Model Selection of Zero-shot Anomaly Detectors in the Absence of Labeled Validation Data [[2024]](https://arxiv.org/abs/2310.10461)
+ PUAD: Frustratingly Simple Method for Robust Anomaly Detection [[2024]](https://arxiv.org/abs/2402.15143)
+ COFT-AD: COntrastive Fine-Tuning for Few-Shot Anomaly Detection [[TIP2024]](http://arxiv.org/abs/2402.18998)
+ PointCore: Efficient Unsupervised Point Cloud Anomaly Detector Using Local-Global Features [[2024]](https://arxiv.org/abs/2403.01804)
+ Learning Unified Reference Representation for Unsupervised Multi-class Anomaly Detection [[2024]](https://arxiv.org/abs/2403.11561)
+ RAD: A Comprehensive Dataset for Benchmarking the Robustness of Image Anomaly Detection [[2024]](https://arxiv.org/abs/2406.07176)
## Medical (related)
+ Towards Universal Unsupervised Anomaly Detection in Medical Imaging [[2024]](http://arxiv.org/pdf/2401.10637v1)
+ MAEDiff: Masked Autoencoder-enhanced Diffusion Models for Unsupervised Anomaly Detection in Brain Images [[2024]](http://arxiv.org/pdf/2401.10561v1)
+ BMAD: Benchmarks for Medical Anomaly Detection [[2023]](https://arxiv.org/abs/2306.11876)
+ Unsupervised Pathology Detection: A Deep Dive Into the State of the Art [[2023]](https://arxiv.org/abs/2303.00609)
+ Adapting Visual-Language Models for Generalizable Anomaly Detection in Medical Images [[CVPR 2024]](https://arxiv.org/abs/2403.12570)
+ Multi-Image Visual Question Answering for Unsupervised Anomaly Detection [[2024]](http://arxiv.org/abs/2404.07622v1)
# Paper Tree (Classification of representative methods)
# Timeline
# Paper list for industrial image anomaly detection
# Related Survey, Benchmark and Framework
+ A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure [[2015]](https://www.sciencedirect.com/science/article/abs/pii/S1474034615000208)
+ Visual-based defect detection and classification approaches for industrial applications: a survey [[2020]](https://pdfs.semanticscholar.org/1dfc/080a5f26b5ce78f9ce3e9f106bf7e8124f74.pdf)
+ Deep Learning for Unsupervised Anomaly Localization in Industrial Images: A Survey [[TIM 2022]](http://arxiv.org/pdf/2207.10298)
+ A Survey on Unsupervised Industrial Anomaly Detection Algorithms [[2022]](https://arxiv.org/abs/2204.11161)
+ A Survey of Methods for Automated Quality Control Based on Images [[IJCV 2023]](https://link.springer.com/article/10.1007/s11263-023-01822-w)[[github page]](https://github.com/jandiers/mvtec-results)
+ Benchmarking Unsupervised Anomaly Detection and Localization [[2022]](https://arxiv.org/abs/2205.14852)
+ IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturing [[TCYB 2024]](https://arxiv.org/abs/2301.13359)[[code]](https://github.com/M-3LAB/open-iad)[[中文]](https://blog.csdn.net/m0_63828250/article/details/136891730)
+ A Deep Learning-based Software for Manufacturing Defect Inspection [[TII 2017]](https://ieeexplore.ieee.org/document/9795891)[[code]](https://github.com/sundyCoder/DEye)
+ Anomalib: A Deep Learning Library for Anomaly Detection [[code]](https://github.com/openvinotoolkit/anomalib)
+ Ph.D. thesis of Paul Bergmann(The first author of MVTec AD series) [[2022]](https://mediatum.ub.tum.de/1662158)
+ CVPR 2023 Tutorial on "Recent Advances in Anomaly Detection" [[CVPR Workshop 2023]](https://sites.google.com/view/cvpr2023-tutorial-on-ad/)[[video]](https://www.youtube.com/watch?v=dXxrzWeybBo&feature=youtu.be)
+ Exploring Plain ViT Reconstruction for Multi-class Unsupervised Anomaly Detection [[2023]](https://arxiv.org/abs/2312.07495)[[code]](https://github.com/zhangzjn/ADer)
+ A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect [[2024]](https://arxiv.org/pdf/2401.16402.pdf)
+ AUPIMO: Redefining Visual Anomaly Detection Benchmarks with High Speed and Low Tolerance [[2024]](https://arxiv.org/abs/2401.01984)
+ Explainable Anomaly Detection in Images and Videos: A Survey [[2024]](https://arxiv.org/pdf/2302.06670)[[repo]](https://github.com/wyzjack/Awesome-XAD)
+ RAD: A Comprehensive Dataset for Benchmarking the Robustness of Image Anomaly Detection [[2024]](https://arxiv.org/abs/2406.07176)
# 2 Unsupervised AD
## 2.1 Feature-Embedding-based Methods
### 2.1.1 Teacher-Student
+ Contextual Affinity Distillation for Image Anomaly Detection [[WACV 2024]](https://openaccess.thecvf.com/content/WACV2024/papers/Zhang_Contextual_Affinity_Distillation_for_Image_Anomaly_Detection_WACV_2024_paper.pdf)
+ Revisiting Reverse Distillation for Anomaly Detection [[CVPR 2023]](https://openaccess.thecvf.com/content/CVPR2023/papers/Tien_Revisiting_Reverse_Distillation_for_Anomaly_Detection_CVPR_2023_paper.pdf) [[code]](https://github.com/tientrandinh/Revisiting-Reverse-Distillation)
+ Uninformed students: Student-teacher anomaly detection with discriminative latent embeddings [[CVPR 2020]](http://arxiv.org/pdf/1911.02357)
+ Multiresolution knowledge distillation for anomaly detection [[CVPR 2021]](https://arxiv.org/pdf/2011.11108)
+ Glancing at the Patch: Anomaly Localization With Global and Local Feature Comparison [[CVPR 2021]](https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Glancing_at_the_Patch_Anomaly_Localization_With_Global_and_Local_CVPR_2021_paper.html)
+ Reconstruction Student with Attention for Student-Teacher Pyramid Matching [[2021]](https://arxiv.org/pdf/2111.15376.pdf)
+ Student-Teacher Feature Pyramid Matching for Anomaly Detection [[2021]](https://arxiv.org/pdf/2103.04257.pdf)[[code]](https://github.com/smiler96/PFM-and-PEFM-for-Image-Anomaly-Detection-and-Segmentation)
+ PFM and PEFM for Image Anomaly Detection and Segmentation [[CASE 2022]](https://ieeexplore.ieee.org/abstract/document/9926547/) [[TII 2022]](https://ieeexplore.ieee.org/document/9795121)[[code]](https://github.com/smiler96/PFM-and-PEFM-for-Image-Anomaly-Detection-and-Segmentation)
+ Reconstructed Student-Teacher and Discriminative Networks for Anomaly Detection [[2022]](https://arxiv.org/pdf/2210.07548.pdf)
+ Anomaly Detection via Reverse Distillation from One-Class Embedding [[CVPR 2022]](http://arxiv.org/pdf/2201.10703)[[code]](https://github.com/hq-deng/RD4AD)
+ Asymmetric Student-Teacher Networks for Industrial Anomaly Detection [[WACV 2022]](https://arxiv.org/pdf/2210.07829.pdf)[[code]](https://github.com/marco-rudolph/AST)
+ Informative knowledge distillation for image anomaly segmentation [[2022]](https://www.sciencedirect.com/science/article/pii/S0950705122004038/pdfft?md5=758c327dd4d1d052b61a19882f957123&pid=1-s2.0-S0950705122004038-main.pdf)[[code]](https://github.com/caoyunkang/IKD)
+ Remembering Normality: Memory-guided Knowledge Distillation for Unsupervised Anomaly Detection [[ICCV 2023]](https://openaccess.thecvf.com/content/ICCV2023/papers/Gu_Remembering_Normality_Memory-guided_Knowledge_Distillation_for_Unsupervised_Anomaly_Detection_ICCV_2023_paper.pdf)
+ A Discrepancy Aware Framework for Robust Anomaly Detection [[2023]](https://arxiv.org/abs/2310.07585)[[code]](https://github.com/caiyuxuan1120/DAF)
+ Enhanced multi-scale features mutual mapping fusion based on reverse knowledge distillation for industrial anomaly detection and localization [[TBD 2024]](https://ieeexplore.ieee.org/abstract/document/10382612)
+ AEKD: Unsupervised auto-encoder knowledge distillation for industrial anomaly detection [[JMS 2024]](https://www.sciencedirect.com/science/article/pii/S0278612524000244)
+ Masked feature regeneration based asymmetric student–teacher network for anomaly detection [[Multimedia Tools and Applications 2024]](https://link.springer.com/article/10.1007/s11042-024-18512-5)
+ Feature-Constrained and Attention-Conditioned Distillation Learning for Visual Anomaly Detection [[ICASSP 2024]](https://ieeexplore.ieee.org/document/10448432)
+ MiniMaxAD: A Lightweight Autoencoder for Feature-Rich Anomaly Detection [[2024]](https://arxiv.org/abs/2405.09933)
### 2.1.2 One-Class Classification (OCC)
+ Patch svdd: Patch-level svdd for anomaly detection and segmentation [[ACCV 2020]](https://arxiv.org/pdf/2006.16067.pdf)
+ Anomaly detection using improved deep SVDD model with data structure preservation [[2021]](https://www.sciencedirect.com/science/article/am/pii/S0167865521001598)
+ A Semantic-Enhanced Method Based On Deep SVDD for Pixel-Wise Anomaly Detection [[2021]](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9428370)
+ MOCCA: Multilayer One-Class Classification for Anomaly Detection [[2021]](http://arxiv.org/pdf/2012.12111)
+ Defect Detection of Metal Nuts Applying Convolutional Neural Networks [[2021]](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9529439)
+ Panda: Adapting pretrained features for anomaly detection and segmentation [[2021]](http://arxiv.org/pdf/2010.05903)
+ Mean-shifted contrastive loss for anomaly detection [[2021]](https://arxiv.org/pdf/2106.03844.pdf)
+ Learning and Evaluating Representations for Deep One-Class Classification [[2020]](https://arxiv.org/pdf/2011.02578.pdf)
+ Self-supervised learning for anomaly detection with dynamic local augmentation [[2021]](https://ieeexplore.ieee.org/ielx7/6287639/6514899/09597511.pdf)
+ Contrastive Predictive Coding for Anomaly Detection [[2021]](https://arxiv.org/pdf/2107.07820.pdf)
+ Cutpaste: Self-supervised learning for anomaly detection and localization [[ICCV 2021]](http://arxiv.org/pdf/2104.04015)[[unofficial code]](https://github.com/Runinho/pytorch-cutpaste)
+ Consistent estimation of the max-flow problem: Towards unsupervised image segmentation [[2020]](http://arxiv.org/pdf/1811.00220)
+ MemSeg: A semi-supervised method for image surface defect detection using differences and commonalities [[2022]](https://arxiv.org/pdf/2205.00908.pdf)[[unofficial code]](https://github.com/TooTouch/MemSeg)
+ SimpleNet: A Simple Network for Image Anomaly Detection and Localization [[CVPR 2023]](https://github.com/DonaldRR/SimpleNet)[[code]](https://github.com/DonaldRR/SimpleNet)
+ End-to-End Augmentation Hyperparameter Tuning for Self-Supervised Anomaly Detection [[2023]](https://arxiv.org/abs/2306.12033)
+ Anomaly Detection under Distribution Shift [[ICCV 2023]](https://openaccess.thecvf.com/content/ICCV2023/papers/Cao_Anomaly_Detection_Under_Distribution_Shift_ICCV_2023_paper.pdf)[[code]](https://github.com/mala-lab/ADShift)
+ Learning Transferable Representations for Image Anomaly Localization Using Dense Pretraining [[WACV 2024]](https://openaccess.thecvf.com/content/WACV2024/papers/He_Learning_Transferable_Representations_for_Image_Anomaly_Localization_Using_Dense_Pretraining_WACV_2024_paper.pdf)[[code]](https://github.com/terrlo/DS2)
+ GeneralAD: Anomaly Detection Across Domains by Attending to Distorted Features [[ECCV 2024]](https://arxiv.org/abs/2407.12427)[[code]](https://github.com/LucStrater/GeneralAD)
+ A Unified Anomaly Synthesis Strategy with Gradient Ascent for Industrial Anomaly Detection and Localization [[ECCV 2024]](https://arxiv.org/abs/2407.09359)[[code]](https://github.com/cqylunlun/GLASS)
### 2.1.3 Distribution-Map
+ Anomaly Detection in Nanofibrous Materials by CNN-Based Self-Similarity [[Sensors 2018]](https://www.mdpi.com/1424-8220/18/1/209)
+ A Multi-Scale A Contrario method for Unsupervised Image Anomaly Detection [[2021]](http://arxiv.org/pdf/2110.02407)
+ Modeling the distribution of normal data in pre-trained deep features for anomaly detection [[2021]](http://arxiv.org/pdf/2005.14140)
+ Transfer Learning Gaussian Anomaly Detection by Fine-Tuning Representations [[2021]](https://arxiv.org/pdf/2108.04116.pdf)
+ PEDENet: Image anomaly localization via patch embedding and density estimation [[2022]](https://arxiv.org/pdf/2110.15525.pdf)
+ Unsupervised image anomaly detection and segmentation based on pre-trained feature mapping [[2022]](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9795121)
+ Position Encoding Enhanced Feature Mapping for Image Anomaly Detection [[2022]](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9926547)[[code]](https://github.com/smiler96/PFM-and-PEFM-for-Image-Anomaly-Detection-and-Segmentation)
+ Focus your distribution: Coarse-to-fine non-contrastive learning for anomaly detection and localization [[ICME 2022]](http://arxiv.org/pdf/2110.04538)
+ Anomaly Detection of Defect using Energy of Point Pattern Features within Random Finite Set Framework [[2021]](https://arxiv.org/abs/2108.12159)[[code]](https://github.com/AmmarKamoona/RFS-Energy-Anomaly-Detection-of-Defect)
+ Fastflow: Unsupervised anomaly detection and localization via 2d normalizing flows [[2021]](https://arxiv.org/pdf/2111.07677.pdf)[[unofficial code]](https://github.com/gathierry/FastFlow)
+ Same same but differnet: Semi-supervised defect detection with normalizing flows [[WACV 2021]](http://arxiv.org/pdf/2008.12577)[[code]](https://github.com/marco-rudolph/differnet)
+ Fully convolutional cross-scale-flows for image-based defect detection [[WACV 2022]](http://arxiv.org/pdf/2110.02855)[[code]](https://github.com/marco-rudolph/cs-flow)
+ Cflow-ad: Real-time unsupervised anomaly detection with localization via conditional normalizing flows [[WACV 2022]](http://arxiv.org/pdf/2107.12571)[[code]](https://github.com/gudovskiy/cflow-ad)
+ CAINNFlow: Convolutional block Attention modules and Invertible Neural Networks Flow for anomaly detection and localization tasks [[2022]](https://arxiv.org/pdf/2206.01992.pdf)
+ AltUB: Alternating Training Method to Update Base Distribution of Normalizing Flow for Anomaly Detection [[2022]](https://arxiv.org/pdf/2210.14913.pdf)
+ Collaborative Discrepancy Optimization for Reliable Image Anomaly Localization [[TII 2023]](https://ieeexplore.ieee.org/document/10034849)[[code]](https://github.com/caoyunkang/CDO)
+ PyramidFlow: High-Resolution Defect Contrastive Localization using Pyramid Normalizing Flow [[CVPR 2023]](https://arxiv.org/abs/2303.02595)[[code]](https://github.com/gasharper/PyramidFlow)
+ Attention Modules Improve Image-Level Anomaly Detection for Industrial Inspection: A DifferNet Case Study [[WACV 2024]](https://openaccess.thecvf.com/content/WACV2024/papers/Vieira_e_Silva_Attention_Modules_Improve_Image-Level_Anomaly_Detection_for_Industrial_Inspection_A_WACV_2024_paper.pdf)
+ Fascinating Supervisory Signals and Where to Find Them: Deep Anomaly Detection with Scale Learning [[ICML 2023]](https://openreview.net/forum?id=V6PNBRWRil)
+ FRAnomaly: flow-based rapid anomaly detection from images [[Applied Intelligence 2024]](https://link.springer.com/article/10.1007/s10489-024-05332-1)
+ Image alignment-based patch distribution framework for anomaly detection [[ICCVDM 2024]](https://www.spiedigitallibrary.org/conference-proceedings-of-spie/13063/130630O/Image-alignment-based-patch-distribution-framework-for-anomaly-detection/10.1117/12.3021499.full)
### 2.1.4 Memory Bank
+ ReConPatch: Contrastive Patch Representation Learning for Industrial Anomaly Detection [[WACV 2024]](https://openaccess.thecvf.com/content/WACV2024/papers/Hyun_ReConPatch_Contrastive_Patch_Representation_Learning_for_Industrial_Anomaly_Detection_WACV_2024_paper.pdf)
+ Sub-image anomaly detection with deep pyramid correspondences [[2020]](https://arxiv.org/pdf/2005.02357.pdf)
+ Semi-orthogonal embedding for efficient unsupervised anomaly segmentation [[2021]](https://arxiv.org/pdf/2105.14737.pdf)
+ Anomaly Detection Via Self-Organizing Map [[2021]](http://arxiv.org/pdf/2107.09903)
+ PaDiM: A Patch Distribution Modeling Framework for Anomaly Detection and Localization [[ICPR 2021]](https://link.springer.com/chapter/10.1007/978-3-030-68799-1_35)[[unofficial code]](https://github.com/xiahaifeng1995/PaDiM-Anomaly-Detection-Localization-master)
+ Industrial Image Anomaly Localization Based on Gaussian Clustering of Pretrained Feature [[2021]](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9479740)
+ Towards total recall in industrial anomaly detection[[CVPR 2022]](http://arxiv.org/pdf/2106.08265)[[code]](https://github.com/amazon-science/patchcore-inspection)
+ CFA: Coupled-Hypersphere-Based Feature Adaptation for Target-Oriented Anomaly Localization[[2022]](https://arxiv.org/pdf/2206.04325.pdf)[[code]](https://github.com/sungwool/CFA_for_anomaly_localization)
+ FAPM: Fast Adaptive Patch Memory for Real-time Industrial Anomaly Detection[[2022]](https://arxiv.org/pdf/2211.07381.pdf)
+ N-pad: Neighboring Pixel-based Industrial Anomaly Detection [[2022]](https://arxiv.org/pdf/2210.08768.pdf)
+ Multi-scale patch-based representation learning for image anomaly detection and segmentation [[2022]](https://openaccess.thecvf.com/content/WACV2022/papers/Tsai_Multi-Scale_Patch-Based_Representation_Learning_for_Image_Anomaly_Detection_and_Segmentation_WACV_2022_paper.pdf)
+ SPot-the-Difference Self-supervised Pre-training for Anomaly Detection and Segmentation [[ECCV 2022]](https://arxiv.org/pdf/2207.14315.pdf)
+ Diversity-Measurable Anomaly Detection [[CVPR 2023]](https://arxiv.org/abs/2303.05047)
+ SelFormaly: Towards Task-Agnostic Unified Anomaly Detection[[2023]](https://arxiv.org/abs/2307.12540)
+ REB: Reducing Biases in Representation for Industrial Anomaly Detection [[2023]](https://arxiv.org/abs/2308.12577)[[code]](https://github.com/ShuaiLYU/REB)
+ PNI : Industrial Anomaly Detection using Position and Neighborhood Information [[ICCV 2023]](https://openaccess.thecvf.com/content/ICCV2023/papers/Bae_PNI__Industrial_Anomaly_Detection_using_Position_and_Neighborhood_Information_ICCV_2023_paper.pdf)[[code]](https://github.com/wogur110/PNI_Anomaly_Detection)
+ Inter-Realization Channels: Unsupervised Anomaly Detection Beyond One-Class Classification [[ICCV 2023]](https://openaccess.thecvf.com/content/ICCV2023/papers/McIntosh_Inter-Realization_Channels_Unsupervised_Anomaly_Detection_Beyond_One-Class_Classification_ICCV_2023_paper.pdf)[[code]](https://github.com/DeclanMcIntosh/InReaCh)
+ Grid-Based Continuous Normal Representation for Anomaly Detection [[2024]](https://arxiv.org/abs/2402.18293)[[code]](https://github.com/tae-mo/GRAD)
+ PointCore: Efficient Unsupervised Point Cloud Anomaly Detector Using Local-Global Features [[2024]](https://arxiv.org/abs/2403.01804)
+ DMAD: Dual Memory Bank for Real-World Anomaly Detection [[2024]](https://arxiv.org/abs/2403.12362)
+ A Reconstruction-Based Feature Adaptation for Anomaly Detection with Self-Supervised Multi-Scale Aggregation [[ICASSP 2024]](https://ieeexplore.ieee.org/document/10446766)
### 2.1.5 Vison Language AD
+ Random Word Data Augmentation with CLIP for Zero-Shot Anomaly Detection [[BMVC 2023]](https://arxiv.org/abs/2308.11119)
+ AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detection [[ICLR 2024]](https://openreview.net/forum?id=buC4E91xZE)[[code]](https://github.com/zqhang/AnomalyCLIP)
+ WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation [[CVPR 2023]](https://arxiv.org/abs/2303.14814)
+ ClipSAM: CLIP and SAM Collaboration for Zero-Shot Anomaly Segmentation [[2023]](https://arxiv.org/pdf/2401.12665)
+ CLIP-AD: A Language-Guided Staged Dual-Path Model for Zero-shot Anomaly Detection [[2023]](https://arxiv.org/abs/2311.00453)
+ AnoVL: Adapting Vision-Language Models for Unified Zero-shot Anomaly Localization [[2023]](https://arxiv.org/abs/2308.15939)[[code]](https://github.com/hq-deng/AnoVL)
+ AnomalyGPT: Detecting Industrial Anomalies using Large Vision-Language Models [[AAAI 2024]](https://arxiv.org/abs/2308.15366)[[code]](https://github.com/CASIA-IVA-Lab/AnomalyGPT)[[project page]](https://anomalygpt.github.io/)
+ Anomaly Detection by Adapting a pre-trained Vision Language Model [[2024]](https://arxiv.org/abs/2403.09493)
+ Customizing Visual-Language Foundation Models for Multi-modal Anomaly Detection and Reasoning [[2024]](https://arxiv.org/abs/2403.11083)[[code]](https://github.com/Xiaohao-Xu/Customizable-VLM)
+ PromptAD: Learning Prompts with only Normal Samples for Few-Shot Anomaly Detection [[CVPR 2024]](https://arxiv.org/abs/2404.05231)[[code]](https://github.com/FuNz-0/PromptAD)
+ Do LLMs Understand Visual Anomalies? Uncovering LLM Capabilities in Zero-shot Anomaly Detection [[2024]](https://arxiv.org/abs/2404.09654)
+ FiLo: Zero-Shot Anomaly Detection by Fine-Grained Description and High-Quality Localization [[2024]](https://arxiv.org/abs/2404.13671)
+ Dual-Image Enhanced CLIP for Zero-Shot Anomaly Detection [[2024]](https://arxiv.org/abs/2405.04782)
## 2.2 Reconstruction-Based Methods
### 2.2.1 Autoencoder (AE)
+ Improving unsupervised defect segmentation by applying structural similarity to autoencoders [[2018]](https://arxiv.org/pdf/1807.02011.pdf)
+ Automatic Fabric Defect Detection with a Multi-Scale Convolutional Denoising Autoencoder Network Model [[Sensors 2018]](https://www.mdpi.com/1424-8220/18/4/1064)
+ An Unsupervised-Learning-Based Approach for Automated Defect Inspection on Textured Surfaces [[TIM 2018]](https://ieeexplore.ieee.org/abstract/document/8281622)
+ Unsupervised anomaly detection using style distillation [[2020]](https://ieeexplore.ieee.org/ielx7/6287639/6514899/09288772.pdf)
+ Unsupervised two-stage anomaly detection [[2021]](https://arxiv.org/pdf/2103.11671.pdf)
+ Dfr: Deep feature reconstruction for unsupervised anomaly segmentation [[Neurocomputing 2020]](https://arxiv.org/pdf/2012.07122.pdf)
+ Unsupervised anomaly segmentation via multilevel image reconstruction and adaptive attention-level transition [[2021]](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9521893)
+ Encoding structure-texture relation with p-net for anomaly detection in retinal images [[2020]](http://arxiv.org/pdf/2008.03632)
+ Improved anomaly detection by training an autoencoder with skip connections on images corrupted with stain-shaped noise [[2021]](http://arxiv.org/pdf/2008.12977)
+ Unsupervised anomaly detection for surface defects with dual-siamese network [[2022]](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9681338)
+ Divide-and-assemble: Learning block-wise memory for unsupervised anomaly detection [[ICCV 2021]](https://openaccess.thecvf.com/content/ICCV2021/papers/Hou_Divide-and-Assemble_Learning_Block-Wise_Memory_for_Unsupervised_Anomaly_Detection_ICCV_2021_paper.pdf)
+ Reconstruction from edge image combined with color and gradient difference for industrial surface anomaly detection [[2022]](http://arxiv.org/pdf/2210.14485)[[code]](https://github.com/liutongkun/edgrec)
+ Spatial Contrastive Learning for Anomaly Detection and Localization [[2022]](https://ieeexplore.ieee.org/ielx7/6287639/9668973/09709224.pdf)
+ Superpixel masking and inpainting for self-supervised anomaly detection [[BMVC 2020]](https://www.bmvc2020-conference.com/assets/papers/0275.pdf)
+ Iterative image inpainting with structural similarity mask for anomaly detection [[2020]](https://openreview.net/pdf?id=b4ach0lGuYO)
+ Self-Supervised Masking for Unsupervised Anomaly Detection and Localization [[2022]](https://arxiv.org/pdf/2205.06568.pdf)
+ Reconstruction by inpainting for visual anomaly detection [[PR 2021]](https://www.sciencedirect.com/science/article/pii/S0031320320305094/pdfft?md5=9bbe942017de1acd3a97034bc2d4a8fb&pid=1-s2.0-S0031320320305094-main.pdf)
+ Draem-a discriminatively trained reconstruction embedding for surface anomaly detection [[ICCV 2021]](http://arxiv.org/pdf/2108.07610)[[code]](https://github.com/vitjanz/draem)
+ DSR: A dual subspace re-projection network for surface anomaly detection [[ECCV 2022]](https://arxiv.org/pdf/2208.01521.pdf)[[code]](https://github.com/VitjanZ/DSR_anomaly_detection)
+ Natural Synthetic Anomalies for Self-supervised Anomaly Detection and Localization [[ECCV 2022]](https://arxiv.org/pdf/2109.15222.pdf)[[code]](https://github.com/hmsch/natural-synthetic-anomalies)
+ Self-Supervised Training with Autoencoders for Visual Anomaly Detection [[2022]](https://arxiv.org/pdf/2206.11723.pdf)
+ Self-supervised predictive convolutional attentive block for anomaly detection [[CVPR 2022 oral]](http://arxiv.org/pdf/2111.09099)[[code]](https://github.com/ristea/sspcab)
+ Self-Supervised Masked Convolutional Transformer Block for Anomaly Detection [[TPAMI 2022]](https://arxiv.org/pdf/2209.12148.pdf)[[code]](https://github.com/ristea/ssmctb)
+ Iterative energy-based projection on a normal data manifold for anomaly localization [[2019]](https://arxiv.org/pdf/2002.03734.pdf)
+ Towards visually explaining variational autoencoders [[2020]](http://arxiv.org/pdf/1911.07389)
+ Deep generative model using unregularized score for anomaly detection with heterogeneous complexity [[2020]](http://arxiv.org/pdf/1807.05800)
+ Anomaly localization by modeling perceptual features [[2020]](https://arxiv.org/pdf/2008.05369.pdf)
+ Image anomaly detection using normal data only by latent space resampling [[2020]](https://pdfs.semanticscholar.org/cb59/dab0a725c0b511f3140ea47ea0967f3643bf.pdf)
+ Noise-to-Norm Reconstruction for Industrial Anomaly Detection and Localization [[2023]](https://arxiv.org/abs/2307.02836)
+ Patch-wise Auto-Encoder for Visual Anomaly Detection [[2023]](https://arxiv.org/abs/2308.00429)
+ FAIR: Frequency-aware Image Restoration for Industrial Visual Anomaly Detection [[2023]](https://arxiv.org/abs/2309.07068)[[code comming soon]](https://github.com/liutongkun/FAIR)
+ Template-guided Hierarchical Feature Restoration for Anomaly Detection [[ICCV 2023]](https://openaccess.thecvf.com/content/ICCV2023/papers/Guo_Template-guided_Hierarchical_Feature_Restoration_for_Anomaly_Detection_ICCV_2023_paper.pdf)
+ FastRecon: Few-shot Industrial Anomaly Detection via Fast Feature Reconstruction [[ICCV 2023]](https://openaccess.thecvf.com/content/ICCV2023/papers/Fang_FastRecon_Few-shot_Industrial_Anomaly_Detection_via_Fast_Feature_Reconstruction_ICCV_2023_paper.pdf)[[code comming soon]](https://github.com/FzJun26th/FastRecon)
+ Produce Once, Utilize Twice for Anomaly Detection [[2023]](https://arxiv.org/abs/2312.12913)
+ RealNet: A Feature Selection Network with Realistic Synthetic Anomaly for Anomaly Detection [[CVPR 2024]](https://arxiv.org/abs/2403.05897)[[code]](https://github.com/cnulab/RealNet)
+ Implicit Foreground-Guided Network for Anomaly Detection and Localization [[ICASSP 2024]](https://ieeexplore.ieee.org/abstract/document/10446952)
+ Neural Network Training Strategy To Enhance Anomaly Detection Performance: A Perspective On Reconstruction Loss Amplification [[ICASSP 2024]](https://ieeexplore.ieee.org/document/10446942)
+ Patch-Wise Augmentation for Anomaly Detection and Localization [[ICASSP 2024]](https://ieeexplore.ieee.org/document/10446994)
+ A Reconstruction-Based Feature Adaptation for Anomaly Detection with Self-Supervised Multi-Scale Aggregation [[ICASSP 2024]](https://ieeexplore.ieee.org/document/10446766)
+ Mixed-Attention Auto Encoder for Multi-Class Industrial Anomaly Detection [[ICASSP 2024]](https://ieeexplore.ieee.org/document/10446794)
+ Dual-Constraint Autoencoder and Adaptive Weighted Similarity Spatial Attention for Unsupervised Anomaly Detection [[TII 2024]](https://ieeexplore.ieee.org/abstract/document/10504620)
+ Multi-feature Reconstruction Network using Crossed-mask Restoration for Unsupervised Anomaly Detection [[2024]](https://arxiv.org/abs/2404.13273)
+ R3D-AD: Reconstruction via Diffusion for 3D Anomaly Detection [[ECCV 2024]](https://arxiv.org/abs/2407.10862)[[homepage]](https://zhouzheyuan.github.io/r3d-ad)
### 2.2.2 Generative Adversarial Networks (GANs)
+ Omni-frequency Channel-selection Representations for Unsupervised Anomaly Detection [[TIP 2023]](https://ieeexplore.ieee.org/abstract/document/10192551/)[[code]](https://github.com/zhangzjn/ocr-gan)
+ Learning semantic context from normal samples for unsupervised anomaly detection [[AAAI 2021]](https://ojs.aaai.org/index.php/AAAI/article/download/16420/16227)
+ Anoseg: Anomaly segmentation network using self-supervised learning [[2021]](https://arxiv.org/pdf/2110.03396.pdf)
+ A Surface Defect Detection Method Based on Positive Samples [[PRICAI 2018]](https://link.springer.com/chapter/10.1007/978-3-319-97310-4_54)
+ Few-shot defect image generation via defect-aware feature manipulation [[AAAI 2023]](https://arxiv.org/abs/2303.02389)[[code]](https://github.com/Ldhlwh/DFMGAN)
### 2.2.3 Transformer
+ VT-ADL: A vision transformer network for image anomaly detection and localization [[ISIE 2021]](http://arxiv.org/pdf/2104.10036)
+ ADTR: Anomaly Detection Transformer with Feature Reconstruction [[2022]](https://arxiv.org/pdf/2209.01816.pdf)
+ AnoViT: Unsupervised Anomaly Detection and Localization With Vision Transformer-Based Encoder-Decoder [[2022]](https://ieeexplore.ieee.org/ielx7/6287639/6514899/09765986.pdf)
+ HaloAE: An HaloNet based Local Transformer Auto-Encoder for Anomaly Detection and Localization [[2022]](https://arxiv.org/pdf/2208.03486.pdf)
+ Inpainting transformer for anomaly detection [[ICIAP 2022]](https://arxiv.org/pdf/2104.13897.pdf)
+ Masked Swin Transformer Unet for Industrial Anomaly Detection [[2022]](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9858596)
+ Masked Transformer for image Anomaly Localization [[TII 2022]](http://arxiv.org/pdf/2210.15540)
+ Focus the Discrepancy: Intra- and Inter-Correlation Learning for Image Anomaly Detection [[ICCV 2023]](https://openaccess.thecvf.com/content/ICCV2023/papers/Yao_Focus_the_Discrepancy_Intra-_and_Inter-Correlation_Learning_for_Image_Anomaly_ICCV_2023_paper.pdf)[[code]](https://github.com/xcyao00/FOD)
+ AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and Localization [[TASE 2024]](https://ieeexplore.ieee.org/abstract/document/10445116)
+ Prior Normality Prompt Transformer for Multi-class Industrial Image Anomaly Detection [[TII 2024]](https://arxiv.org/abs/2406.11507)
### 2.2.4 Diffusion Model
+ AnoDDPM: Anomaly Detection With Denoising Diffusion Probabilistic Models Using Simplex Noise [[CVPR Workshop 2022]](http://dro.dur.ac.uk/36134/1/36134.pdf)
+ Unsupervised Visual Defect Detection with Score-Based Generative Model[[2022]](https://arxiv.org/pdf/2211.16092.pdf)
+ DiffusionAD: Denoising Diffusion for Anomaly Detection [[2023]](https://arxiv.org/abs/2303.08730)[[code]](https://github.com/HuiZhang0812/DiffusionAD)
+ Anomaly Detection with Conditioned Denoising Diffusion Models [[2023]](https://arxiv.org/abs/2305.15956)
+ Unsupervised Surface Anomaly Detection with Diffusion Probabilistic Model [[ICCV 2023]](https://openaccess.thecvf.com/content/ICCV2023/papers/Zhang_Unsupervised_Surface_Anomaly_Detection_with_Diffusion_Probabilistic_Model_ICCV_2023_paper.pdf)
+ Removing Anomalies as Noises for Industrial Defect Localization [[ICCV 2023]](https://openaccess.thecvf.com/content/ICCV2023/papers/Lu_Removing_Anomalies_as_Noises_for_Industrial_Defect_Localization_ICCV_2023_paper.pdf)
+ TransFusion -- A Transparency-Based Diffusion Model for Anomaly Detection [[ECCV 2024]](https://arxiv.org/abs/2311.09999)[[code]](https://github.com/MaticFuc/ECCV_TransFusion)
+ LafitE: Latent Diffusion Model with Feature Editing for Unsupervised Multi-class Anomaly Detection [[2023]](https://arxiv.org/abs/2307.08059)
+ DiAD: A Diffusion-based Framework for Multi-class Anomaly Detection [[AAAI 2024]](https://ojs.aaai.org/index.php/AAAI/article/view/28690)[[code]](https://lewandofskee.github.io/projects/diad)
+ D3AD: Dynamic Denoising Diffusion Probabilistic Model for Anomaly Detection [[2024]](https://arxiv.org/abs/2401.04463)
+ GLAD: Towards Better Reconstruction with Global and Local Adaptive Diffusion Models for Unsupervised Anomaly Detection [[ECCV 2024]](https://arxiv.org/abs/2406.07487)[[code]](https://github.com/hyao1/GLAD)
### 2.2.5 Others
+ Anomaly Detection using Score-based Perturbation Resilience [[ICCV 2023]](https://openaccess.thecvf.com/content/ICCV2023/papers/Shin_Anomaly_Detection_using_Score-based_Perturbation_Resilience_ICCV_2023_paper.pdf)
# 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]](https://www.ri.cmu.edu/wp-content/uploads/2020/05/WenHsuan_MSR_Thesis-1.pdf)
+ Explainable Deep One-Class Classification [[ICLR 2020]](https://arxiv.org/pdf/2007.01760.pdf)
+ Attention guided anomaly localization in images [[ECCV 2020]](http://arxiv.org/pdf/1911.08616)
+ Mixed supervision for surface-defect detection: From weakly to fully supervised learning [[2021]](https://arxiv.org/pdf/2104.06064.pdf)
+ Explainable deep few-shot anomaly detection with deviation networks [[2021]](https://arxiv.org/pdf/2108.00462.pdf)[[code]](https://github.com/Choubo/deviation-network-image)
+ Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detection [[CVPR 2022]](http://arxiv.org/pdf/2203.14506)[[code]](https://github.com/Choubo/DRA)
+ Anomaly Clustering: Grouping Images into Coherent Clusters of Anomaly Types[[WACV 2023]](https://openaccess.thecvf.com/content/WACV2023/html/Sohn_Anomaly_Clustering_Grouping_Images_Into_Coherent_Clusters_of_Anomaly_Types_WACV_2023_paper.html)
+ Prototypical Residual Networks for Anomaly Detection and Localization [[CVPR 2023]](https://arxiv.org/abs/2212.02031)[[code]](https://github.com/xcyao00/PRNet)
+ Efficient Anomaly Detection with Budget Annotation Using Semi-Supervised Residual Transformer [[2023]](https://arxiv.org/abs/2306.03492)
+ Anomaly Heterogeneity Learning for Open-set Supervised Anomaly Detection [[CVPR 2024]](https://arxiv.org/abs/2310.12790)[[code]](https://github.com/mala-lab/AHL)
+ Few-shot defect image generation via defect-aware feature manipulation [[AAAI 2023]](https://arxiv.org/abs/2303.02389)[[code]](https://github.com/Ldhlwh/DFMGAN)
+ AnomalyDiffusion: Few-Shot Anomaly Image Generation with Diffusion Model [[AAAI 2024]](https://ojs.aaai.org/index.php/AAAI/article/view/28696)[[code]](https://github.com/sjtuplayer/anomalydiffusion)
+ BiaS: Incorporating Biased Knowledge to Boost Unsupervised Image Anomaly Localization [[TSMC 2024]](https://ieeexplore.ieee.org/abstract/document/10402554)
+ DMAD: Dual Memory Bank for Real-World Anomaly Detection [[2024]](https://arxiv.org/abs/2403.12362)
## 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]](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9475966)
+ Interleaved Deep Artifacts-Aware Attention Mechanism for Concrete Structural Defect Classification [[TIP 2021]](https://eprints.keele.ac.uk/10031/1/TIP24Jul2021.pdf)
+ Reference-based defect detection network [[TIP 2021]](http://arxiv.org/pdf/2108.04456)
+ Fabric defect detection using tactile information [[ICRA 2021]](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9561092)
+ A lightweight spatial and temporal multi-feature fusion network for defect detection [[TIP 2020]](http://nrl.northumbria.ac.uk/id/eprint/48908/1/ALightweightSpatialandTemporalMulti-featureFusionNetworkforDefectDetection.pdf)
+ SDD-CNN: Small Data-Driven Convolution Neural Networks for Subtle Roller Defect Inspection [[Robotics and Computer-Integrated Manufacturing 2020]](https://www.sciencedirect.com/science/article/abs/pii/S0736584518304770)
+ A High-Efficiency Fully Convolutional Networks for Pixel-Wise Surface Defect Detection [[IEEE Access 2019]](https://ieeexplore.ieee.org/abstract/document/8624360)
+ SDD-CNN: Small Data-Driven Convolution Neural Networks for Subtle Roller Defect Inspection [[Applied Sciences 2019]](https://www.mdpi.com/2076-3417/9/7/1364)
+ Autonomous Structural Visual Inspection Using Region-Based Deep Learning for Detecting Multiple Damage Types [[CACIE 2018]](https://dl.acm.org/doi/abs/10.1111/mice.12334)
+ Detection and segmentation of manufacturing defects with convolutional neural networks and transfer learning [[2018]](https://europepmc.org/articles/pmc6512995?pdf=render)
+ Automatic Metallic Surface Defect Detection and Recognition with Convolutional Neural Networks [[Applied Sciences 2018]](https://www.mdpi.com/2076-3417/8/9/1575)
+ Real-time Detection of Steel Strip Surface Defects Based on Improved YOLO Detection Network [[IFAC-PapersOnLine 2018]](https://www.sciencedirect.com/science/article/pii/S2405896318321001)
+ Domain adaptation for automatic OLED panel defect detection using adaptive support vector data description [[IJCV 2017]](https://link.springer.com/article/10.1007/s11263-016-0953-y)
+ Automatic Defect Detection of Fasteners on the Catenary Support Device Using Deep Convolutional Neural Network [[TIM 2017]](https://ieeexplore.ieee.org/abstract/document/8126877)
+ Deep Active Learning for Civil Infrastructure Defect Detection and Classification [Computing in civil engineering 2017](https://ascelibrary.org/doi/abs/10.1061/9780784480823.036)
+ A fast and robust convolutional neural network-based defect detection model in product quality control [[IJAMT 2017]](https://link.springer.com/article/10.1007/s00170-017-0882-0)
+ Defects Detection Based on Deep Learning and Transfer Learning [[Metallurgical & Mining Industry 2015]](https://web.s.ebscohost.com/abstract?direct=true&profile=ehost&scope=site&authtype=crawler&jrnl=20760507&AN=115932631&h=Xxf%2binGAfPaFG1E3Net%2fQQIu5U%2fD2pFkichv9fJ63Bx%2bjW2wr5y1UZWYaHbOQCE%2bZc%2bYJQz117Xd06J3IxAbSg%3d%3d&crl=c&resultNs=AdminWebAuth&resultLocal=ErrCrlNotAuth&crlhashurl=login.aspx%3fdirect%3dtrue%26profile%3dehost%26scope%3dsite%26authtype%3dcrawler%26jrnl%3d20760507%26AN%3d115932631)
+ Design of deep convolutional neural network architectures for automated feature extraction in industrial inspection [[CIRP annals 2016]](https://www.sciencedirect.com/science/article/abs/pii/S0007850616300725)
+ Decision Fusion Network with Perception Fine-tuning for Defect Classification [[2023]](https://arxiv.org/abs/2309.12630)
+ Global Context Aggregation Network for Lightweight Saliency Detection of Surface Defects [[2023]](https://arxiv.org/abs/2309.12641)
+ Dual Attention U-Net with Feature Infusion: Pushing the Boundaries of Multiclass Defect Segmentation [[2023]](https://arxiv.org/abs/2312.14053)[[code]](https://github.com/RashaAlshawi/Dual-Attention-U-Net-with-Feature-Infusion-Pushing-the-Boundaries-of-Multiclass-Defect-Segmentation)
+ MemoryMamba: Memory-Augmented State Space Model for Defect Recognition [[2024]](https://arxiv.org/abs/2405.03673)
+ Supervised Anomaly Detection for Complex Industrial Images [[2024]](https://arxiv.org/abs/2405.04953)[[code]](https://github.com/abc-125/segad)
# 3 Other Research Direction
## 3.1 Few-Shot AD
+ Learning unsupervised metaformer for anomaly detection [[ICCV 2021]](https://openaccess.thecvf.com/content/ICCV2021/papers/Wu_Learning_Unsupervised_Metaformer_for_Anomaly_Detection_ICCV_2021_paper.pdf)
+ Registration based few-shot anomaly detection [[ECCV 2022 oral]](https://arxiv.org/pdf/2207.07361.pdf)[[code]](https://github.com/MediaBrain-SJTU/RegAD)
+ Same same but differnet: Semi-supervised defect detection with normalizing flows [[(Distribution)WACV 2021]](http://arxiv.org/pdf/2008.12577)
+ Towards total recall in industrial anomaly detection [[(Memory bank)CVPR 2022]](http://arxiv.org/pdf/2106.08265)
+ A hierarchical transformation-discriminating generative model for few shot anomaly detection [[ICCV 2021]](http://arxiv.org/pdf/2104.14535)
+ Anomaly detection of defect using energy of point pattern features within random finite set framework [[2021]](https://arxiv.org/pdf/2108.12159.pdf)
+ Optimizing PatchCore for Few/many-shot Anomaly Detection [[2023]](https://arxiv.org/abs/2307.10792)[[code]](https://github.com/scortexio/patchcore-few-shot/)
+ AnomalyGPT: Detecting Industrial Anomalies using Large Vision-Language Models [[AAAI 2024]](https://arxiv.org/abs/2308.15366)[[code]](https://github.com/CASIA-IVA-Lab/AnomalyGPT)[[project page]](https://anomalygpt.github.io/)
+ FastRecon: Few-shot Industrial Anomaly Detection via Fast Feature Reconstruction [[ICCV 2023]](https://openaccess.thecvf.com/content/ICCV2023/papers/Fang_FastRecon_Few-shot_Industrial_Anomaly_Detection_via_Fast_Feature_Reconstruction_ICCV_2023_paper.pdf)[[code comming soon]](https://github.com/FzJun26th/FastRecon)
+ Produce Once, Utilize Twice for Anomaly Detection [[2023]](https://arxiv.org/abs/2312.12913)
+ COFT-AD: COntrastive Fine-Tuning for Few-Shot Anomaly Detection [[TIP2024]](http://arxiv.org/abs/2402.18998)
+ Text-Guided Variational Image Generation for Industrial Anomaly Detection and Segmentation [[CVPR 2024]](https://arxiv.org/abs/2403.06247)
+ Multimodal Industrial Anomaly Detection by Crossmodal Feature Mapping [[CVPR 2024]](https://arxiv.org/abs/2312.04521)
+ Dual-path Frequency Discriminators for Few-shot Anomaly Detection [[2024]](https://arxiv.org/abs/2403.04151)
+ Few-shot Online Anomaly Detection and Segmentation [[2024]](https://arxiv.org/abs/2403.18201)
+ FewSOME: One-Class Few Shot Anomaly Detection with Siamese Networks [[CVPRW 2023]](https://openaccess.thecvf.com/content/CVPR2023W/VAND/papers/Belton_FewSOME_One-Class_Few_Shot_Anomaly_Detection_With_Siamese_Networks_CVPRW_2023_paper.pdf)[[code]](https://github.com/niamhbelton/FewSOME)
+ AnomalyDINO: Boosting Patch-based Few-shot Anomaly Detection with DINOv2 [[2024]](https://arxiv.org/abs/2405.14529)
### Zero-Shot AD
+ Random Word Data Augmentation with CLIP for Zero-Shot Anomaly Detection [[BMVC 2023]](https://arxiv.org/abs/2308.11119)
+ Zero-Shot Batch-Level Anomaly Detection [[2023]](https://arxiv.org/abs/2302.07849)
+ Zero-shot versus Many-shot: Unsupervised Texture Anomaly Detection [[WACV 2023]](https://ieeexplore.ieee.org/document/10030870)
+ MAEDAY: MAE for few and zero shot AnomalY-Detection [[2022]](https://arxiv.org/pdf/2211.14307.pdf)
+ WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation [[CVPR 2023]](https://arxiv.org/abs/2303.14814)
+ Segment Any Anomaly without Training via Hybrid Prompt Regularization [[2023]](https://arxiv.org/abs/2305.10724) [[code]](https://github.com/caoyunkang/GroundedSAM-zero-shot-anomaly-detection)
+ Anomaly Detection in an Open World by a Neuro-symbolic Program on Zero-shot Symbols [[IROS 2022 Workshop]](https://openreview.net/pdf?id=Bg3ZO3nXJuA)
+ 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 [[CVPR 2023 VAND Workshop Challenge]](https://arxiv.org/abs/2305.17382)
+ AnoVL: Adapting Vision-Language Models for Unified Zero-shot Anomaly Localization [[2023]](https://arxiv.org/abs/2308.15939)[[code]](https://github.com/hq-deng/AnoVL)
+ CLIP-AD: A Language-Guided Staged Dual-Path Model for Zero-shot Anomaly Detection [[2023]](https://arxiv.org/abs/2311.00453)
+ PromptAD: Zero-shot Anomaly Detection using Text Prompts [[WACV 2024]](https://openaccess.thecvf.com/content/WACV2024/papers/Li_PromptAD_Zero-Shot_Anomaly_Detection_Using_Text_Prompts_WACV_2024_paper.pdf)
+ High-Fidelity Zero-Shot Texture Anomaly Localization Using Feature Correspondence Analysis [[WACV 2024]](https://openaccess.thecvf.com/content/WACV2024/html/Ardelean_High-Fidelity_Zero-Shot_Texture_Anomaly_Localization_Using_Feature_Correspondence_Analysis_WACV_2024_paper.html)
+ AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detection [[ICLR 2024]](https://openreview.net/forum?id=buC4E91xZE)[[code]](https://github.com/zqhang/AnomalyCLIP)
+ MuSc: Zero-Shot Industrial Anomaly Classification and Segmentation with Mutual Scoring of the Unlabeled Images[[ICLR 2024]](https://openreview.net/forum?id=AHgc5SMdtd)[[code]](https://github.com/xrli-U/MuSc)
+ ClipSAM: CLIP and SAM Collaboration for Zero-Shot Anomaly Segmentation [[2023]](https://arxiv.org/pdf/2401.12665)
+ 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 [[2023]](https://arxiv.org/abs/2305.17382)
+ Model Selection of Zero-shot Anomaly Detectors in the Absence of Labeled Validation Data [[2024]](https://arxiv.org/abs/2310.10461)
+ Toward Generalist Anomaly Detection via In-context Residual Learning with Few-shot Sample Prompts [[CVPR 2024]](https://arxiv.org/abs/2403.06495)[[code]](https://github.com/mala-lab/InCTRL)
+ PromptAD: Learning Prompts with only Normal Samples for Few-Shot Anomaly Detection [[CVPR 2024]](https://arxiv.org/abs/2404.05231)[[code]](https://github.com/FuNz-0/PromptAD)
+ Do LLMs Understand Visual Anomalies? Uncovering LLM Capabilities in Zero-shot Anomaly Detection [[2024]](https://arxiv.org/abs/2404.09654)
+ FiLo: Zero-Shot Anomaly Detection by Fine-Grained Description and High-Quality Localization [[2024]](https://arxiv.org/abs/2404.13671)
+ Dual-Image Enhanced CLIP for Zero-Shot Anomaly Detection [[2024]](https://arxiv.org/abs/2405.04782)
+ Investigating the Semantic Robustness of CLIP-based Zero-Shot Anomaly Segmentation [[2024]](https://arxiv.org/abs/2405.07969)
+ SAM-LAD: Segment Anything Model Meets Zero-Shot Logic Anomaly Detection [[2024]](https://arxiv.org/abs/2406.00625)
+ VCP-CLIP: A visual context prompting model for zero-shot anomaly segmentation [[ECCV 2024]](https://arxiv.org/abs/2407.12276)[[code]](https://github.com/xiaozhen228/VCP-CLIP)
+ AdaCLIP: Adapting CLIP with Hybrid Learnable Prompts for Zero-Shot Anomaly Detection [[ECCV 2024]](https://arxiv.org/abs/2407.15795)[[code]](https://github.com/caoyunkang/AdaCLIP)
## 3.2 Noisy AD
+ Trustmae: A noise-resilient defect classification framework using memory-augmented auto-encoders with trust regions [[WACV 2021]](http://arxiv.org/pdf/2012.14629)
+ Self-Supervise, Refine, Repeat: Improving Unsupervised Anomaly Detection [[TMLR 2021]](https://arxiv.org/pdf/2106.06115.pdf)
+ Data refinement for fully unsupervised visual inspection using pre-trained networks [[2022]](https://arxiv.org/pdf/2202.12759.pdf)
+ Latent Outlier Exposure for Anomaly Detection with Contaminated Data [[ICML 2022]](https://arxiv.org/pdf/2202.08088.pdf)
+ Deep one-class classification via interpolated gaussian descriptor [[AAAI 2022 oral]](https://arxiv.org/pdf/2101.10043.pdf)[[code]](https://github.com/tianyu0207/IGD)
+ SoftPatch: Unsupervised Anomaly Detection with Noisy Data [[NeurIPS 2022]](https://openreview.net/pdf?id=pIYYJflkhZ))[[code]](https://github.com/TencentYoutuResearch/AnomalyDetection-SoftPatch)
+ Inter-Realization Channels: Unsupervised Anomaly Detection Beyond One-Class Classification [[ICCV 2023]](https://openaccess.thecvf.com/content/ICCV2023/papers/McIntosh_Inter-Realization_Channels_Unsupervised_Anomaly_Detection_Beyond_One-Class_Classification_ICCV_2023_paper.pdf)[[code]](https://github.com/DeclanMcIntosh/InReaCh)
+ M3DM-NR: RGB-3D Noisy-Resistant Industrial Anomaly Detection via Multimodal Denoising [[2024]](https://arxiv.org/abs/2406.02263)
## 3.3 Anomaly Synthetic
+ Cutpaste: Self-supervised learning for anomaly detection and localization [[(OCC)ICCV 2021]](http://arxiv.org/pdf/2104.04015)[[unofficial code]](https://github.com/Runinho/pytorch-cutpaste)
+ Draem-a discriminatively trained reconstruction embedding for surface anomaly detection [[(Reconstruction AE)ICCV 2021]](http://arxiv.org/pdf/2108.07610)[[code]](https://github.com/vitjanz/draem)
+ DSR: A dual subspace re-projection network for surface anomaly detection [[ECCV 2022]](https://arxiv.org/pdf/2208.01521.pdf)[[code]](https://github.com/VitjanZ/DSR_anomaly_detection)
+ Natural Synthetic Anomalies for Self-supervised Anomaly Detection and Localization [[ECCV 2022]](https://arxiv.org/pdf/2109.15222.pdf)[[code]](https://github.com/hmsch/natural-synthetic-anomalies)
+ MemSeg: A semi-supervised method for image surface defect detection using differences and commonalities [[(OCC)2022]](https://arxiv.org/pdf/2205.00908.pdf)[[unofficial code]](https://github.com/TooTouch/MemSeg)
+ A High-Efficiency Fully Convolutional Networks for Pixel-Wise Surface Defect Detection [[IEEE Access 2019]](https://ieeexplore.ieee.org/abstract/document/8624360)
+ Multistage GAN for fabric defect detection [[2019]](https://pubmed.ncbi.nlm.nih.gov/31870985/)
+ Gan-based defect synthesis for anomaly detection in fabrics [[2020]](https://www.lfb.rwth-aachen.de/bibtexupload/pdf/RIP20c.pdf)
+ Defect image sample generation with GAN for improving defect recognition [[2020]](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9000806)
+ Defective samples simulation through neural style transfer for automatic surface defect segment [[2020]](http://arxiv.org/pdf/1910.03334)
+ A simulation-based few samples learning method for surface defect segmentation [[2020]](https://www.sciencedirect.com/science/article/pii/S0925231220310791/pdfft?md5=f3f72bc8687c8f9968d4a2a1bd3ea17e&pid=1-s2.0-S0925231220310791-main.pdf)
+ Synthetic data augmentation for surface defect detection and classification using deep learning [[2020]](https://link.springer.com/article/10.1007/s10845-020-01710-x)
+ Defect Transfer GAN: Diverse Defect Synthesis for Data Augmentation [[BMVC 2022]](https://openreview.net/pdf?id=2hMEdc35xZ6)
+ Defect-GAN: High-fidelity defect synthesis for automated defect inspection [[2021]](https://dr.ntu.edu.sg/bitstream/10356/146285/2/WACV_2021_Defect_GAN__Camera_Ready_.pdf)
+ EID-GAN: Generative Adversarial Nets for Extremely Imbalanced Data Augmentation[[TII 2022]](https://ieeexplore.ieee.org/document/9795891)
+ Multilevel Saliency-Guided Self-Supervised Learning for Image Anomaly Detection [[2023]](http://arxiv.org/pdf/2311.18332v1)
+ DeSTSeg: Segmentation Guided Denoising Student-Teacher for Anomaly Detection [[CVPR 2023]](https://arxiv.org/abs/2211.11317)[[code]](https://github.com/apple/ml-destseg)
+ AnomalyDiffusion: Few-Shot Anomaly Image Generation with Diffusion Model [[AAAI 2024]](https://ojs.aaai.org/index.php/AAAI/article/view/28696)[[code]](https://github.com/sjtuplayer/anomalydiffusion)
+ RealNet: A Feature Selection Network with Realistic Synthetic Anomaly for Anomaly Detection [[CVPR 2024]](https://arxiv.org/abs/2403.05897)[[code]](https://github.com/cnulab/RealNet)
+ Dual-path Frequency Discriminators for Few-shot Anomaly Detection [[2024]](https://arxiv.org/abs/2403.04151)
+ A Novel Approach to Industrial Defect Generation through Blended Latent Diffusion Model with Online Adaptation [[2024]](https://arxiv.org/abs/2402.19330)[[code]](https://github.com/GrandpaXun242/AdaBLDM)
+ A Comprehensive Augmentation Framework for Anomaly Detection [[AAAI 2024]](https://ojs.aaai.org/index.php/AAAI/article/view/28720)
+ CAGEN: Controllable Anomaly Generator using Diffusion Model [[ICASSP 2024]](https://ieeexplore.ieee.org/document/10447663)
+ AnomalyXFusion: Multi-modal Anomaly Synthesis with Diffusion [[2024]](https://arxiv.org/abs/2404.19444)[[data]](https://github.com/hujiecpp/MVTec-Caption)
+ Few-shot defect image generation via defect-aware feature manipulation [[AAAI 2023]](https://arxiv.org/abs/2303.02389)[[code]](https://github.com/Ldhlwh/DFMGAN)
+ A Unified Anomaly Synthesis Strategy with Gradient Ascent for Industrial Anomaly Detection and Localization [[ECCV 2024]](https://arxiv.org/abs/2407.09359)[[code]](https://github.com/cqylunlun/GLASS)
## 3.4 RGBD AD
+ Anomaly detection in 3d point clouds using deep geometric descriptors [[WACV 2022]](https://arxiv.org/pdf/2202.11660.pdf)
+ Back to the feature: classical 3d features are (almost) all you need for 3D anomaly detection [[2022]](https://arxiv.org/pdf/2203.05550.pdf)[[code]](https://github.com/eliahuhorwitz/3D-ADS)
+ Anomaly Detection Requires Better Representations [[2022]](https://arxiv.org/pdf/2210.10773.pdf)
+ Asymmetric Student-Teacher Networks for Industrial Anomaly Detection [[WACV 2022]](https://arxiv.org/pdf/2210.07829.pdf)
+ Multimodal Industrial Anomaly Detection via Hybrid Fusion [[CVPR 2023]](https://arxiv.org/abs/2303.00601)
+ Complementary Pseudo Multimodal Feature for Point Cloud Anomaly Detection [[2023]](https://arxiv.org/abs/2303.13194)[[code]](https://github.com/caoyunkang/CPMF)
+ Image-Pointcloud Fusion based Anomaly Detection using PD-REAL Dataset [[2023]](https://arxiv.org/abs/2311.04095)[[data]](https://github.com/Andy-cs008/PD-REAL)
+ Towards Scalable 3D Anomaly Detection and Localization: A Benchmark via 3D Anomaly Synthesis and A Self-Supervised Learning Network [[CVPR 2024]](https://arxiv.org/abs/2311.14897)[[code]](https://github.com/Chopper-233/Anomaly-ShapeNet)
+ Shape-Guided Dual-Memory Learning for 3D Anomaly Detection [[ICML 2023]](https://openreview.net/forum?id=IkSGn9fcPz)
+ EasyNet: An Easy Network for 3D Industrial Anomaly Detection [[ACM MM 2023]](https://arxiv.org/abs/2307.13925)
+ Self-supervised Feature Adaptation for 3D Industrial Anomaly Detection [[2024]](https://arxiv.org/abs/2401.03145)
+ Cheating Depth: Enhancing 3D Surface Anomaly Detection via Depth Simulation [[WACV 2024]](https://openaccess.thecvf.com/content/WACV2024/papers/Zavrtanik_Cheating_Depth_Enhancing_3D_Surface_Anomaly_Detection_via_Depth_Simulation_WACV_2024_paper.pdf)[[code]](https://github.com/VitjanZ/3DSR)
+ Incremental Template Neighborhood Matching for 3D anomaly detection [[Neurocomputing 2024]](https://www.sciencedirect.com/science/article/abs/pii/S0925231224002546)
+ Keep DRÆMing: Discriminative 3D anomaly detection through anomaly simulation [[PRL 2024]](https://www.sciencedirect.com/science/article/pii/S0167865524000862)
+ Rethinking Reverse Distillation for Multi-Modal Anomaly Detection [[AAAI 2024]](https://ojs.aaai.org/index.php/AAAI/article/view/28687)
+ Multimodal Industrial Anomaly Detection by Crossmodal Feature Mapping [[CVPR 2024]](https://arxiv.org/abs/2312.04521)
+ Cross-Modal Distillation in Industrial Anomaly Detection: Exploring Efficient Multi-Modal IAD [[2024]](https://arxiv.org/abs/2405.13571)[[code]](https://github.com/evenrose/CMDIAD)
+ M3DM-NR: RGB-3D Noisy-Resistant Industrial Anomaly Detection via Multimodal Denoising [[2024]](https://arxiv.org/abs/2406.02263)
## 3.5 3D AD
+ Real3D-AD: A Dataset of Point Cloud Anomaly Detection [[NeurIPS 2023]](https://arxiv.org/abs/2309.13226)[[code]](https://github.com/M-3LAB/Real3D-AD)
+ PointCore: Efficient Unsupervised Point Cloud Anomaly Detector Using Local-Global Features [[2024]](https://arxiv.org/abs/2403.01804)
+ Towards Scalable 3D Anomaly Detection and Localization: A Benchmark via 3D Anomaly Synthesis and A Self-Supervised Learning Network [[CVPR 2024]](https://arxiv.org/abs/2311.14897)[[code]](https://github.com/Chopper-233/Anomaly-ShapeNet)
+ R3D-AD: Reconstruction via Diffusion for 3D Anomaly Detection [[ECCV 2024]](https://arxiv.org/abs/2407.10862)[[homepage]](https://zhouzheyuan.github.io/r3d-ad)
## 3.6 Continual AD
+ Towards Total Online Unsupervised Anomaly Detection and Localization in Industrial Vision [[2023]](https://arxiv.org/abs/2305.15652)
+ Towards Continual Adaptation in Industrial Anomaly Detection [[ACM MM 2022]](https://dl.acm.org/doi/abs/10.1145/3503161.3548232)
+ An Incremental Unified Framework for Small Defect Inspection [[2023]](https://arxiv.org/abs/2312.08917)[[code]](https://github.com/jqtangust/IUF)
+ Unsupervised Continual Anomaly Detection with Contrastively-learned Prompt [[AAAI 2024]](https://ojs.aaai.org/index.php/AAAI/article/view/28153)[[code]](https://github.com/shirowalker/UCAD)
## 3.7 Uniform/Multi-Class AD
+ A Unified Model for Multi-class Anomaly Detection [[NeurIPS 2022]](https://arxiv.org/pdf/2206.03687.pdf) [[code]](https://github.com/zhiyuanyou/UniAD)
+ OmniAL A unifiled CNN framework for unsupervised anomaly localization [[CVPR 2023]](https://openaccess.thecvf.com/content/CVPR2023/papers/Zhao_OmniAL_A_Unified_CNN_Framework_for_Unsupervised_Anomaly_Localization_CVPR_2023_paper.pdf)
+ SelFormaly: Towards Task-Agnostic Unified Anomaly Detection[[2023]](https://arxiv.org/abs/2307.12540)
+ Hierarchical Vector Quantized Transformer for Multi-class Unsupervised Anomaly Detection [[NeurIPS 2023]](https://openreview.net/pdf?id=clJTNssgn6)[[code]](https://github.com/RuiyingLu/HVQ-Trans)
+ Removing Anomalies as Noises for Industrial Defect Localization [[ICCV 2023]](https://openaccess.thecvf.com/content/ICCV2023/papers/Lu_Removing_Anomalies_as_Noises_for_Industrial_Defect_Localization_ICCV_2023_paper.pdf)
+ UniFormaly: Towards Task-Agnostic Unified Framework for Visual Anomaly Detection [[2023]](https://arxiv.org/abs/2307.12540)[[code]](https://github.com/YoojLee/Uniformaly)
+ MSTAD: A masked subspace-like transformer for multi-class anomaly detection [[2023]](https://www.sciencedirect.com/science/article/pii/S095070512300936X)
+ LafitE: Latent Diffusion Model with Feature Editing for Unsupervised Multi-class Anomaly Detection [[2023]](https://arxiv.org/abs/2307.08059)
+ DiAD: A Diffusion-based Framework for Multi-class Anomaly Detection [[AAAI 2024]](https://ojs.aaai.org/index.php/AAAI/article/view/28690)[[code]](https://lewandofskee.github.io/projects/diad)
+ Exploring Plain ViT Reconstruction for Multi-class Unsupervised Anomaly Detection [[2023]](https://arxiv.org/abs/2312.07495)
+ Structural Teacher-Student Normality Learning for Multi-Class Anomaly Detection and Localization [[2024]](https://arxiv.org/abs/2402.17091)
+ Unsupervised anomaly detection and localization with one model for all category [[KBS 2024]](https://www.sciencedirect.com/science/article/pii/S0950705124001680)
+ Anomaly Detection by Adapting a pre-trained Vision Language Model [[2024]](https://arxiv.org/abs/2403.09493)
+ DMAD: Dual Memory Bank for Real-World Anomaly Detection [[2024]](https://arxiv.org/abs/2403.12362)
+ Toward Multi-class Anomaly Detection: Exploring Class-aware Unified Model against Inter-class Interference [[2024]](https://arxiv.org/abs/2403.14213)
+ Hierarchical Gaussian Mixture Normalizing Flow Modeling for Unified Anomaly Detection [[ECCV 2024]](https://arxiv.org/abs/2403.13349)[[code]](https://github.com/xcyao00/HGAD)
+ Long-Tailed Anomaly Detection with Learnable Class Names [[CVPR 2024]](https://arxiv.org/abs/2403.20236)[[data split]](https://zenodo.org/records/10854201)
+ MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection [[2024]](https://arxiv.org/abs/2404.06564)[[code]](https://github.com/lewandofskee/MambaAD)
+ Learning Feature Inversion for Multi-class Anomaly Detection under General-purpose COCO-AD Benchmark [[2024]](https://arxiv.org/abs/2404.10760)[[code]](https://github.com/zhangzjn/ader)
+ Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detection [[2024]](https://arxiv.org/abs/2405.14325)
+ Prior Normality Prompt Transformer for Multi-class Industrial Image Anomaly Detection [[TII 2024]](https://arxiv.org/abs/2406.11507)
+ An Incremental Unified Framework for Small Defect Inspection [[ECCV2024]](https://arxiv.org/abs/2312.08917v2)[[code]](https://github.com/jqtangust/IUF)
+ Learning Multi-view Anomaly Detection [[2024]](https://arxiv.org/abs/2407.11935)
## 3.8 Logical AD
+ Beyond Dents and Scratches: Logical Constraints in Unsupervised Anomaly Detection and Localization [[IJCV 2022]](https://link.springer.com/content/pdf/10.1007/s11263-022-01578-9.pdf)
+ Set Features for Fine-grained Anomaly Detection[[2023]](https://arxiv.org/abs/2302.12245) [[code]](https://github.com/NivC/SINBAD)
+ SLSG: Industrial Image Anomaly Detection by Learning Better Feature Embeddings and One-Class Classification [[2023]](https://arxiv.org/abs/2305.00398)
+ EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies [[WACV 2024]](https://openaccess.thecvf.com/content/WACV2024/papers/Batzner_EfficientAD_Accurate_Visual_Anomaly_Detection_at_Millisecond-Level_Latencies_WACV_2024_paper.pdf)
+ Contextual Affinity Distillation for Image Anomaly Detection [[WACV 2024]](https://openaccess.thecvf.com/content/WACV2024/papers/Zhang_Contextual_Affinity_Distillation_for_Image_Anomaly_Detection_WACV_2024_paper.pdf)
+ REB: Reducing Biases in Representation for Industrial Anomaly Detection [[2023]](https://arxiv.org/abs/2308.12577)[[code]](https://github.com/ShuaiLYU/REB)
+ Learning Global-Local Correspondence with Semantic Bottleneck for Logical Anomaly Detection [[TCSVT 2023]](https://arxiv.org/abs/2303.05768)[[code]](https://github.com/hmyao22/GLCF)
+ Template-guided Hierarchical Feature Restoration for Anomaly Detection [[ICCV 2023]](https://openaccess.thecvf.com/content/ICCV2023/papers/Guo_Template-guided_Hierarchical_Feature_Restoration_for_Anomaly_Detection_ICCV_2023_paper.pdf)
+ Few Shot Part Segmentation Reveals Compositional Logic for Industrial Anomaly Detection [[AAAI 2024]](https://ojs.aaai.org/index.php/AAAI/article/view/28703)[[code]](https://github.com/oopil/PSAD_logical_anomaly_detection)
+ Generating and Reweighting Dense Contrastive Patterns for Unsupervised Anomaly Detection [[AAAI 2024]](https://ojs.aaai.org/index.php/AAAI/article/view/27910)
+ PUAD: Frustratingly Simple Method for Robust Anomaly Detection [[2024]](https://arxiv.org/abs/2402.15143)
+ AnomalyXFusion: Multi-modal Anomaly Synthesis with Diffusion [[2024]](https://arxiv.org/abs/2404.19444)[[data]](https://github.com/hujiecpp/MVTec-Caption)
+ Supervised Anomaly Detection for Complex Industrial Images [[2024]](https://arxiv.org/abs/2405.04953)[[code]](https://github.com/abc-125/segad)
+ SAM-LAD: Segment Anything Model Meets Zero-Shot Logic Anomaly Detection [[2024]](https://arxiv.org/abs/2406.00625)
## Other settings
### TTT binary segmentation
Test Time Training for Industrial Anomaly Segmentation [[2024]](https://arxiv.org/abs/2404.03743)
# 4 Dataset
| Dataset | Class | Normal | Abnormal | Total | Annotation level | Source | Time |
|------------------------|-------|--------|----------|--------|-------------------|-----------------------|--------------|
| [AITEX](https://www.cvmart.net/dataSets/detail/300) | 1 | 140 | 105 | 245 | Segmentation mask | RGB real | 2019 |
| [Anomaly-ShapeNet](https://arxiv.org/abs/2311.14897)[[data]](https://github.com/Chopper-233/Anomaly-ShapeNet) | 40 | - | - | 1600 | Point-level mask | Point-cloud synthetic | CVPR,2024 |
| [BTAD](http://avires.dimi.uniud.it/papers/btad/btad.zip) | 3 | - | - | 2830 | Segmentation mask | RGB real | 2021 |
| [CID](https://github.com/LightZH/Insulator-Defect-Detection) | 1 | 4060 | 233 | 4293 | Segmentation mask | RGB real | 2024,TIM |
| [DAGM](https://www.kaggle.com/datasets/mhskjelvareid/dagm-2007-competition-dataset-optical-inspection) | 10 | - | - | 11500 | Segmentation mask | RGB synthetic | 2007 |
| [DEEPPCB](https://github.com/tangsanli5201/DeepPCB) | 1 | - | - | 1500 | Bounding box | RGB synthetic | 2019 |
| [DTD-Synthetic](https://drive.google.com/drive/folders/10OyPzvI3H6llCZBxKxFlKWt1Pw1tkMK1) | 12 | - | - | - | Segmentation mask | RGB synthetic | WACV,2024 |
| [Eyecandies](https://eyecan-ai.github.io/eyecandies/) | 10 | 13250 | 2250 | 15500 | Segmentation mask | RGBD synthetic image | ACCV,2022 |
| [Fabirc dataset](http://hub.hku.hk/bitstream/10722/229176/1/content.pdf) | 1 | 25 | 25 | 50 | Segmentation mask | RGB synthetic | PR,2016 |
| [GDXray](https://domingomery.ing.puc.cl/material/gdxray/) | 1 | 0 | 19407 | 19407 | Bounding box | RGB real | 2016 |
| [IPAD](https://ljf1113.github.io/IPAD_VAD/) | 16 | - | - | 597979 | Image | Video real&synthetic | 2024 |
| [KolekrotSDD](https://www.vicos.si/resources/kolektorsdd/) | 1 | 347 | 52 | 399 | Segmentation mask | RGB real | JIM,2019 |
| [KolekrotSDD2](https://www.vicos.si/resources/kolektorsdd2/) | 1 | 2979 | 356 | 3335 | Segmentation mask | RGB real | CiI,2021 |
| [MIAD](https://miad-2022.github.io/) | 7 | 87500 | 17500 | 105000 | Segmentation mask | RGB synthetic | 2023 |
| [MPDD](https://vutbr-my.sharepoint.com/personal/xjezek16_vutbr_cz/_layouts/15/onedrive.aspx?id=%2Fpersonal%2Fxjezek16%5Fvutbr%5Fcz%2FDocuments%2FMPDD&ga=1) | 6 | 1064 | 282 | 1346 | Segmentation mask | RGB real | ICUMT,2021 |
| [MTD](https://github.com/abin24/Magnetic-tile-defect-datasets.) | 1 | 952 | 392 | 1344 | Segmentation mask | RGB real | CASE,2018 |
| [MVTec AD](https://www.mvtec.com/company/research/datasets/mvtec-ad) | 15 | 4096 | 1258 | 5354 | Segmentation mask | RGB real | CVPR,2019 |
| [MVTec 3D-AD](https://www.mvtec.com/company/research/datasets/mvtec-3d-ad) | 10 | 2904 | 948 | 3852 | Segmentation mask | RGB real | VISAPP,2021 |
| [MVTec LOCO-AD](https://www.mvtec.com/company/research/datasets/mvtec-loco) | 5 | 2347 | 993 | 3340 | Segmentation mask | RGBD real | IJCV,2022 |
| [NanoTwice](http://web.mi.imati.cnr.it/ettore/NanoTwice) | 1 | 5 | 40 | 45 | Segmentation mask | RGB real | TII,2016 |
| [NEU surface defect](http://faculty.neu.edu.cn/songkechen/zh_CN/zdylm/263270/list/index.htm) | 1 | 0 | 1800 | 1800 | Bounding box | RGB real | 2013 |
| [PAD](https://github.com/EricLee0224/PAD) | 20 | 5231 | 4902 | 10133 | Segmentation mask | RBG synthetic | NeurIPS,2023 |
| [Real-IAD](https://realiad4ad.github.io/Real-IAD/) | 30 | 99721 | 51329 | 151050 | Segmentation mask | RGB real | CVPR,2024 |
| [Real3D-AD](https://github.com/M-3LAB/Real3D-AD) | 12 | 652 | 602 | 1254 | Point-level mask | Point-cloud real | NeurIPS,2023 |
| [RSDD](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8063875) | 2 | - | - | 195 | Segmentation mask | RGB real | 2017 |
| [Steel defect detection](https://www.kaggle.com/code/ekhtiar/resunet-a-baseline-on-tensorflow/notebook) | 1 | - | - | 18076 | Image | RGB real | 2019 |
| [Steel tube dataset](https://github.com/huangyebiaoke/steel-pipe-weld-defect-detection) | 1 | 0 | 3408 | 3408 | Bounding box | RGB real | 2021 |
| [VisA](https://github.com/amazon-science/spot-diff) | 12 | 9621 | 1200 | 10821 | Segmentation mask | RGB real | ECCV,2022 |
| [RAD](https://github.com/hustCYQ/RAD-dataset) | 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]](https://www.sciencedirect.com/science/article/pii/S0169433213016437/pdfft?md5=478bf7f07bbf551a5d991048f9bc16e4&pid=1-s2.0-S0169433213016437-main.pdf) [[data]](http://faculty.neu.edu.cn/songkechen/zh_CN/zdylm/263270/list/index.htm)
+ (Steel tube dataset)Deep learning based steel pipe weld defect detection [[2021]](https://www.tandfonline.com/doi/pdf/10.1080/08839514.2021.1975391?needAccess=true) [[data]](https://github.com/huangyebiaoke/steel-pipe-weld-defect-detection)
+ (Steel defect dataset)Severstal: Steel Defect Detection [[data 2019]](https://www.kaggle.com/code/ekhtiar/resunet-a-baseline-on-tensorflow/notebook)
+ (NanoTwice)Defect detection in SEM images of nanofibrous materials [[TII 2016]](https://re.public.polimi.it/bitstream/11311/1024586/1/anomaly_detection_sem.pdf) [[data]](http://web.mi.imati.cnr.it/ettore/NanoTwice)
+ (GDXray)GDXray: The database of X-ray images for nondestructive testing [[2015]](http://dmery.sitios.ing.uc.cl/Prints/ISI-Journals/2015-JNDE-GDXray.pdf) [[data]](https://domingomery.ing.puc.cl/material/gdxray/)
+ (DEEP PCB)Online PCB defect detector on a new PCB defect dataset [[2019]](https://arxiv.org/pdf/1902.06197.pdf) [[data]](https://github.com/tangsanli5201/DeepPCB)
+ (PCBA-defect) A PCB Dataset for Defects Detection and Classification [[2019]](https://arxiv.org/abs/1901.08204)[[data]](https://www.kaggle.com/datasets/akhatova/pcb-defects)
+ (CPLID) Insulator Data Set - Chinese Power Line Insulator Dataset [[data]](https://github.com/InsulatorData/InsulatorDataSet)
+ (Fabric dataset)Fabric inspection based on the Elo rating method [[PR 2016]](http://hub.hku.hk/bitstream/10722/229176/1/content.pdf)
+ (KolektorSDD)Segmentation-based deep-learning approach for surface-defect detection [[Journal of Intelligent Manufacturing]](http://arxiv.org/pdf/1903.08536) [[data]](https://www.vicos.si/resources/kolektorsdd/)
+ (KolektorSDD2)Mixed supervision for surface-defect detection: From weakly to fully supervised learning [[Computers in Industry 2021]](https://arxiv.org/pdf/2104.06064.pdf) [[data]](https://www.vicos.si/resources/kolektorsdd2/)
+ (RSDD)A hierarchical extractor-based visual rail surface inspection system [[2017]](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8063875)
+ (Eyecandies)The Eyecandies Dataset for Unsupervised Multimodal Anomaly Detection and Localization [[ACCV 2022]](https://arxiv.org/pdf/2210.04570.pdf) [[data]](https://eyecan-ai.github.io/eyecandies/)
+ (MVTec AD)MVTec AD: A comprehensive real-world dataset for unsupervised anomaly detection [[CVPR 2019]](https://openaccess.thecvf.com/content_CVPR_2019/html/Bergmann_MVTec_AD_--_A_Comprehensive_Real-World_Dataset_for_Unsupervised_Anomaly_CVPR_2019_paper.html) [[IJCV 2021]](https://link.springer.com/content/pdf/10.1007/s11263-020-01400-4.pdf) [[data]](https://www.mvtec.com/company/research/datasets/mvtec-ad)
+ (MVTec 3D-AD)The mvtec 3d-ad dataset for unsupervised 3d anomaly detection and localization [[VISAPP 2021]](https://arxiv.org/pdf/2112.09045.pdf) [[data]](https://www.mvtec.com/company/research/datasets/mvtec-3d-ad)
+ (MVTec LOCO-AD)Beyond Dents and Scratches: Logical Constraints in Unsupervised Anomaly Detection and Localization [[IJCV 2022]](https://link.springer.com/content/pdf/10.1007/s11263-022-01578-9.pdf) [[data]](https://www.mvtec.com/company/research/datasets/mvtec-loco)
+ (MPDD)Deep learning-based defect detection of metal parts: evaluating current methods in complex conditions [[ICUMT 2021]](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9631567) [[data]](https://vutbr-my.sharepoint.com/personal/xjezek16_vutbr_cz/_layouts/15/onedrive.aspx?id=%2Fpersonal%2Fxjezek16%5Fvutbr%5Fcz%2FDocuments%2FMPDD&ga=1)
+ (BTAD)VT-ADL: A vision transformer network for image anomaly detection and localization [[2021]](http://arxiv.org/pdf/2104.10036) [[data]](http://avires.dimi.uniud.it/papers/btad/btad.zip)
+ (VisA)SPot-the-Difference Self-supervised Pre-training for Anomaly Detection and Segmentation [[ECCV 2022]](https://arxiv.org/pdf/2207.14315.pdf) [[data]](https://github.com/amazon-science/spot-diff)
+ (MTD)Surface defect saliency of magnetic tile [[2020]](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8560423) [[data]](https://github.com/abin24/Magnetic-tile-defect-datasets.)
+ (DAGM)DAGM dataset [[data 2007]](https://www.kaggle.com/datasets/mhskjelvareid/dagm-2007-competition-dataset-optical-inspection)
+ (MIAD)Miad:A maintenance inspection dataset for unsupervised anomaly detection [[2022]](https://arxiv.org/abs/2211.13968) [[data]](https://miad-2022.github.io/)
+ CVPR 1st workshop on Vision-based InduStrial InspectiON [[homepage]](https://vision-based-industrial-inspection.github.io/cvpr-2023/) [[data]](https://drive.google.com/drive/folders/1TVp_UXJuXudqhC2L3ZKyIDcmQ_2O3JVi)
+ (SSGD)SSGD: A smartphone screen glass dataset for defect detection [[2023]](https://arxiv.org/abs/2303.06673)[[dataset is coming soon]](https://github.com/Yangr116/SSGDataset)
+ (AeBAD)Industrial Anomaly Detection with Domain Shift: A Real-world Dataset and Masked Multi-scale Reconstruction [[2023]](https://arxiv.org/abs/2304.02216) [[data]](https://github.com/zhangzilongc/MMR)
+ VISION Datasets: A Benchmark for Vision-based InduStrial InspectiON [[2023]](https://arxiv.org/abs/2306.07890) [[data]](https://huggingface.co/datasets/VISION-Workshop/VISION-Datasets)
+ PAD: A Dataset and Benchmark for Pose-agnostic Anomaly Detection [[NeurIPS 2023]](https://github.com/EricLee0224/PAD)
+ PKU-GoodsAD: A Supermarket Goods Dataset for Unsupervised Anomaly Detection and Segmentation [[2023]](https://github.com/jianzhang96/GoodsAD)[[data]](https://github.com/jianzhang96/GoodsAD)
+ Real3D-AD: A Dataset of Point Cloud Anomaly Detection [[NeurIPS 2023]](https://openreview.net/pdf?id=zGthDp4yYe)[[data]](https://github.com/M-3LAB/Real3D-AD)
+ InsPLAD: A Dataset and Benchmark for Power Line Asset Inspection in UAV Images [[IJRS 2023]](https://arxiv.org/abs/2311.01619)[[data]](https://github.com/andreluizbvs/InsPLAD)
+ Image-Pointcloud Fusion based Anomaly Detection using PD-REAL Dataset [[2023]](https://arxiv.org/abs/2311.04095)[[data]](https://github.com/Andy-cs008/PD-REAL)
+ CrashCar101: Procedural Generation for Damage Assessment [[WACV 2024]](https://crashcar.compute.dtu.dk/static/2435.pdf)[[data]](https://crashcar.compute.dtu.dk/)
+ Defect Spectrum: A Granular Look of Large-Scale Defect Datasets with Rich Semantics [[2023]](https://openreview.net/forum?id=RLhS1TrjK3)[[data]](https://defect-spectrum-authors.github.io/defect-spectrum/)
+ (DTD-Synthetic) Zero-shot versus Many-shot: Unsupervised Texture Anomaly Detection [[WACV 2023]](https://ieeexplore.ieee.org/document/10030870)[[data]](https://drive.google.com/drive/folders/10OyPzvI3H6llCZBxKxFlKWt1Pw1tkMK1)
+ Towards Scalable 3D Anomaly Detection and Localization: A Benchmark via 3D Anomaly Synthesis and A Self-Supervised Learning Network [[CVPR 2024]](https://arxiv.org/abs/2311.14897)[[data]](https://github.com/Chopper-233/Anomaly-ShapeNet)
+ Real-IAD: A Real-World Multi-view Dataset for Benchmarking Versatile Industrial Anomaly Detection [[CVPR 2024]](https://arxiv.org/abs/2403.12580)[[code]](https://github.com/Tencent/AnomalyDetection_Real-IAD)[[data]](https://realiad4ad.github.io/Real-IAD/)
+ Catenary Insulator Defects Detection: A Dataset and an Unsupervised Baseline [[TIM 2024]](https://ieeexplore.ieee.org/abstract/document/10504848)[[code]](https://github.com/LightZH/Insulator-Defect-Detection)
+ IPAD: Industrial Process Anomaly Detection Dataset [[2024]](https://arxiv.org/abs/2404.15033)[[data]](https://ljf1113.github.io/IPAD_VAD/)
+ MVTec-Caption: AnomalyXFusion: Multi-modal Anomaly Synthesis with Diffusion [[2024]](https://arxiv.org/abs/2404.19444)[[data]](https://github.com/hujiecpp/MVTec-Caption)
+ Supervised Anomaly Detection for Complex Industrial Images [[2024]](https://arxiv.org/abs/2405.04953)[[data coming]](https://github.com/abc-125/segad)
## BibTex Citation
If you find this paper and repository useful, please cite our paper☺️.
```
@article{liu2024deep,
title={Deep industrial image anomaly detection: A survey},
author={Liu, Jiaqi and Xie, Guoyang and Wang, Jinbao and Li, Shangnian and Wang, Chengjie and Zheng, Feng and Jin, Yaochu},
journal={Machine Intelligence Research},
volume={21},
number={1},
pages={104--135},
year={2024},
publisher={Springer}
}
@article{xie2024iad,
title={Im-iad: Industrial image anomaly detection benchmark in manufacturing},
author={Xie, Guoyang and Wang, Jinbao and Liu, Jiaqi and Lyu, Jiayi and Liu, Yong and Wang, Chengjie and Zheng, Feng and Jin, Yaochu},
journal={IEEE Transactions on Cybernetics},
year={2024},
publisher={IEEE}
}
@article{jiang2022survey,
title={A survey of visual sensory anomaly detection},
author={Jiang, Xi and Xie, Guoyang and Wang, Jinbao and Liu, Yong and Wang, Chengjie and Zheng, Feng and Jin, Yaochu},
journal={arXiv preprint arXiv:2202.07006},
year={2022}
}
```
## Star History
[](https://star-history.com/#M-3LAB/awesome-industrial-anomaly-detection&Date)