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Deep Industrial Image Anomaly Detection: A Survey (Machine Intelligence Research)
IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturing [TCYB 2024][code][中文]
We will keep focusing on this field and updating relevant information.
Keywords: anomaly detection, anomaly segmentation, industrial image, defect detection
[Main Page] [Survey] [Benchmark] [Result]
- Implicit Foreground-Guided Network for Anomaly Detection and Localization [ICASSP 2024]
- Neural Network Training Strategy To Enhance Anomaly Detection Performance: A Perspective On Reconstruction Loss Amplification [ICASSP 2024]
- Patch-Wise Augmentation for Anomaly Detection and Localization [ICASSP 2024]
- A Reconstruction-Based Feature Adaptation for Anomaly Detection with Self-Supervised Multi-Scale Aggregation [ICASSP 2024]
- Feature-Constrained and Attention-Conditioned Distillation Learning for Visual Anomaly Detection [ICASSP 2024]
- CAGEN: Controllable Anomaly Generator using Diffusion Model [ICASSP 2024]
- Mixed-Attention Auto Encoder for Multi-Class Industrial Anomaly Detection [ICASSP 2024]
- Text-Guided Variational Image Generation for Industrial Anomaly Detection and Segmentation [CVPR 2024]
- RealNet: A Feature Selection Network with Realistic Synthetic Anomaly for Anomaly Detection [CVPR 2024][code]
- Toward Generalist Anomaly Detection via In-context Residual Learning with Few-shot Sample Prompts [CVPR 2024][code]
- Multimodal Industrial Anomaly Detection by Crossmodal Feature Mapping [CVPR 2024]
- Towards Scalable 3D Anomaly Detection and Localization: A Benchmark via 3D Anomaly Synthesis and A Self-Supervised Learning Network [CVPR 2024][code]
- Real-IAD: A Real-World Multi-view Dataset for Benchmarking Versatile Industrial Anomaly Detection [CVPR 2024][code][data]
- Long-Tailed Anomaly Detection with Learnable Class Names [CVPR 2024][data split]
- PromptAD: Learning Prompts with only Normal Samples for Few-Shot Anomaly Detection [CVPR 2024][code]
- Supervised Anomaly Detection for Complex Industrial Images [CVPR 2024 comming]
- CVPRW: VAND 2.0: Visual Anomaly and Novelty Detection - 2nd Edition [Challenge and Call for Papers]
- AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detection [ICLR 2024][code]
- MuSc: Zero-Shot Anomaly Classification and Segmentation by Mutual Scoring of the Unlabeled Images[ICLR 2024][code]
- Rethinking Reverse Distillation for Multi-Modal Anomaly Detection [AAAI 2024]
- Unsupervised Continual Anomaly Detection with Contrastively-learned Prompt [AAAI 2024][code]
- Few Shot Part Segmentation Reveals Compositional Logic for Industrial Anomaly Detection [AAAI 2024]
- DiAD: A Diffusion-based Framework for Multi-class Anomaly Detection [AAAI 2024][code]
- Generating and Reweighting Dense Contrastive Patterns for Unsupervised Anomaly Detection [AAAI 2024]
- AnomalyDiffusion: Few-Shot Anomaly Image Generation with Diffusion Model [AAAI 2024][code]
- AnomalyGPT: Detecting Industrial Anomalies using Large Vision-Language Models [AAAI 2024][code][project page]
- A Comprehensive Augmentation Framework for Anomaly Detection [AAAI 2024]
- ReConPatch: Contrastive Patch Representation Learning for Industrial Anomaly Detection [WACV 2024]
- Learning Transferable Representations for Image Anomaly Localization Using Dense Pretraining [WACV 2024][code]
- EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies [WACV 2024]
- Contextual Affinity Distillation for Image Anomaly Detection [WACV 2024]
- Attention Modules Improve Image-Level Anomaly Detection for Industrial Inspection: A DifferNet Case Study [WACV 2024]
- PromptAD: Zero-shot Anomaly Detection using Text Prompts [WACV 2024]
- High-Fidelity Zero-Shot Texture Anomaly Localization Using Feature Correspondence Analysis [WACV 2024]
- Cheating Depth: Enhancing 3D Surface Anomaly Detection via Depth Simulation [WACV 2024][code]
- Real3D-AD: A Dataset of Point Cloud Anomaly Detection [NeurIPS 2023][code][中文]
- PAD: A Dataset and Benchmark for Pose-agnostic Anomaly Detection [NeurIPS 2023][code]
- Zero-Shot Anomaly Detection via Batch Normalization [NeurIPS 2023][code]
- SANFlow: Semantic-Aware Normalizing Flow for Anomaly Detection and Localization [NeurIPS 2023]
- Energy-Based Models for Anomaly Detection: A Manifold Diffusion Recovery Approach [NeurIPS 2023]
- Hierarchical Vector Quantized Transformer for Multi-class Unsupervised Anomaly Detection [NeurIPS 2023][code]
- ReContrast: Domain-Specific Anomaly Detection via Contrastive Reconstruction [NeurIPS 2023][code]
- Shape-Guided Dual-Memory Learning for 3D Anomaly Detection [ICML 2023]
- Fascinating Supervisory Signals and Where to Find Them: Deep Anomaly Detection with Scale Learning [ICML 2023]
- EasyNet: An Easy Network for 3D Industrial Anomaly Detection [ACM MM 2023]
- Remembering Normality: Memory-guided Knowledge Distillation for Unsupervised Anomaly Detection [ICCV 2023]
- Unsupervised Surface Anomaly Detection with Diffusion Probabilistic Model [ICCV 2023]
- PNI: Industrial Anomaly Detection using Position and Neighborhood Information [ICCV 2023][code]
- Anomaly Detection using Score-based Perturbation Resilience [ICCV 2023]
- Template-guided Hierarchical Feature Restoration for Anomaly Detection [ICCV 2023]
- Focus the Discrepancy: Intra- and Inter-Correlation Learning for Image Anomaly Detection [ICCV 2023][code]
- Anomaly Detection under Distribution Shift [ICCV 2023][code]
- FastRecon: Few-shot Industrial Anomaly Detection via Fast Feature Reconstruction [ICCV 2023][code comming soon]
- Inter-Realization Channels: Unsupervised Anomaly Detection Beyond One-Class Classification [ICCV 2023][code]
- Removing Anomalies as Noises for Industrial Defect Localization [ICCV 2023]
- Myriad: Large Multimodal Model by Applying Vision Experts for Industrial Anomaly Detection [2023][code]
- AnomalyGPT: Detecting Industrial Anomalies using Large Vision-Language Models [AAAI 2024][code][project page]
- The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision) [2023 Section 9.2]
- Towards Generic Anomaly Detection and Understanding: Large-scale Visual-linguistic Model (GPT-4V) Takes the Lead [2023][code]
- Exploring Grounding Potential of VQA-oriented GPT-4V for Zero-shot Anomaly Detection [2023][code]
- Customizing Visual-Language Foundation Models for Multi-modal Anomaly Detection and Reasoning [2024]
- Do LLMs Understand Visual Anomalies? Uncovering LLM Capabilities in Zero-shot Anomaly Detection [2024]
- CVPR 2023 Tutorial on "Recent Advances in Anomaly Detection" [CVPR Workshop 2023(mainly on video anomaly detection)][video]
- Workshop on Vision-Based Industrial Inspection [CVPR Workshop paper list 2023]
- Visual Anomaly and Novelty Detection [CVPR Workshop paper list 2023]
- Revisiting Reverse Distillation for Anomaly Detection [CVPR 2023] [code]
- OmniAL A unifiled CNN framework for unsupervised anomaly localization [CVPR 2023]
- Explicit Boundary Guided Semi-Push-Pull Contrastive Learning for Supervised Anomaly Detection [CVPR 2023][code]
- DeSTSeg: Segmentation Guided Denoising Student-Teacher for Anomaly Detection [CVPR 2023][code]
- Diversity-Measurable Anomaly Detection [CVPR 2023]
- WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation [CVPR 2023]
- SimpleNet: A Simple Network for Image Anomaly Detection and Localization [CVPR 2023][code]
- PyramidFlow: High-Resolution Defect Contrastive Localization using Pyramid Normalizing Flow [CVPR 2023][code]
- Multimodal Industrial Anomaly Detection via Hybrid Fusion [CVPR 2023][code]
- Prototypical Residual Networks for Anomaly Detection and Localization [CVPR 2023][code]
- SQUID: Deep Feature In-Painting for Unsupervised Anomaly Detection [CVPR 2023]
- APRIL-GAN: A Zero-/Few-Shot Anomaly Classification and Segmentation Method for CVPR 2023 VAND Workshop Challenge Tracks 1&2: 1st Place on Zero-shot AD and 4th Place on Few-shot AD [CVPR 2023 VAND Workshop Challenge]
- Segment Anything Is Not Always Perfect: An Investigation of SAM on Different Real-world Applications [2023 SAM tech report]
- SAM Struggles in Concealed Scenes -- Empirical Study on "Segment Anything" [2023 SAM tech report]
- Segment Any Anomaly without Training via Hybrid Prompt Regularization [2023] [code]
- Application of Segment Anything Model for Civil Infrastructure Defect Assessment [2023 SAM tech report]
- Segment Anything in Defect Detection [2023]
- Unsupervised Continual Anomaly Detection with Contrastively-learned Prompt [AAAI 2024][code]
- ClipSAM: CLIP and SAM Collaboration for Zero-Shot Anomaly Segmentation [2023]
- A SAM-guided Two-stream Lightweight Model for Anomaly Detection [2024]
- Pushing the Limits of Fewshot Anomaly Detection in Industry Vision: Graphcore [ICLR 2023]
- RGI: robust GAN-inversion for mask-free image inpainting and unsupervised pixel-wise anomaly detection [ICLR 2023]
- Self-supervised Context Learning for Visual Inspection of Industrial Defects [2023][code]
- CLIP-AD: A Language-Guided Staged Dual-Path Model for Zero-shot Anomaly Detection [2023]
- Self-Tuning Self-Supervised Anomaly Detection [2023]
- Defect Spectrum: A Granular Look of Large-Scale Defect Datasets with Rich Semantics [2023][data]
- Model Selection of Anomaly Detectors in the Absence of Labeled Validation Data [2023]
- A Discrepancy Aware Framework for Robust Anomaly Detection [2023][code]
- The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision) [2023 Section 9.2]
- Global Context Aggregation Network for Lightweight Saliency Detection of Surface Defects [2023]
- Decision Fusion Network with Perception Fine-tuning for Defect Classification [2023]
- FAIR: Frequency-aware Image Restoration for Industrial Visual Anomaly Detection [2023][code]
- AnoVL: Adapting Vision-Language Models for Unified Zero-shot Anomaly Localization [2023][code]
- End-to-End Augmentation Hyperparameter Tuning for Self-Supervised Anomaly Detection [2023]
- CVPR 1st workshop on Vision-based InduStrial InspectiON [CVPR 2023 Workshop] [data link]
- Multilevel Saliency-Guided Self-Supervised Learning for Image Anomaly Detection [2023]
- How Low Can You Go? Surfacing Prototypical In-Distribution Samples for Unsupervised Anomaly Detection [Dataset Distillation][2023]
- Exploring Plain ViT Reconstruction for Multi-class Unsupervised Anomaly Detection [2023]
- AUPIMO: Redefining Visual Anomaly Detection Benchmarks with High Speed and Low Tolerance [2024]
- Model Selection of Zero-shot Anomaly Detectors in the Absence of Labeled Validation Data [2024]
- PUAD: Frustratingly Simple Method for Robust Anomaly Detection [2024]
- COFT-AD: COntrastive Fine-Tuning for Few-Shot Anomaly Detection [TIP2024]
- PointCore: Efficient Unsupervised Point Cloud Anomaly Detector Using Local-Global Features [2024]
- Learning Unified Reference Representation for Unsupervised Multi-class Anomaly Detection [2024]
- Towards Universal Unsupervised Anomaly Detection in Medical Imaging [2024]
- MAEDiff: Masked Autoencoder-enhanced Diffusion Models for Unsupervised Anomaly Detection in Brain Images [2024]
- BMAD: Benchmarks for Medical Anomaly Detection [2023]
- Unsupervised Pathology Detection: A Deep Dive Into the State of the Art [2023]
- Adapting Visual-Language Models for Generalizable Anomaly Detection in Medical Images [CVPR 2024]
- Multi-Image Visual Question Answering for Unsupervised Anomaly Detection [2024]
- A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure [2015]
- Visual-based defect detection and classification approaches for industrial applications: a survey [2020]
- Deep Learning for Unsupervised Anomaly Localization in Industrial Images: A Survey [TIM 2022]
- A Survey on Unsupervised Industrial Anomaly Detection Algorithms [2022]
- A Survey of Methods for Automated Quality Control Based on Images [IJCV 2023][github page]
- Benchmarking Unsupervised Anomaly Detection and Localization [2022]
- IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturing [TCYB 2024][code][中文]
- A Deep Learning-based Software for Manufacturing Defect Inspection [TII 2017][code]
- Anomalib: A Deep Learning Library for Anomaly Detection [code]
- Ph.D. thesis of Paul Bergmann(The first author of MVTec AD series) [2022]
- CVPR 2023 Tutorial on "Recent Advances in Anomaly Detection" [CVPR Workshop 2023][video]
- Exploring Plain ViT Reconstruction for Multi-class Unsupervised Anomaly Detection [2023][code]
- A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect [2024]
- AUPIMO: Redefining Visual Anomaly Detection Benchmarks with High Speed and Low Tolerance [2024]
- Contextual Affinity Distillation for Image Anomaly Detection [WACV 2024]
- Revisiting Reverse Distillation for Anomaly Detection [CVPR 2023] [code]
- Uninformed students: Student-teacher anomaly detection with discriminative latent embeddings [CVPR 2020]
- Multiresolution knowledge distillation for anomaly detection [CVPR 2021]
- Glancing at the Patch: Anomaly Localization With Global and Local Feature Comparison [CVPR 2021]
- Reconstruction Student with Attention for Student-Teacher Pyramid Matching [2021]
- Student-Teacher Feature Pyramid Matching for Anomaly Detection [2021][code]
- PFM and PEFM for Image Anomaly Detection and Segmentation [CASE 2022] [TII 2022][code]
- Reconstructed Student-Teacher and Discriminative Networks for Anomaly Detection [2022]
- Anomaly Detection via Reverse Distillation from One-Class Embedding [CVPR 2022][code]
- Asymmetric Student-Teacher Networks for Industrial Anomaly Detection [WACV 2022][code]
- Informative knowledge distillation for image anomaly segmentation [2022][code]
- Remembering Normality: Memory-guided Knowledge Distillation for Unsupervised Anomaly Detection [ICCV 2023]
- A Discrepancy Aware Framework for Robust Anomaly Detection [2023][code]
- Enhanced multi-scale features mutual mapping fusion based on reverse knowledge distillation for industrial anomaly detection and localization [TBD 2024]
- AEKD: Unsupervised auto-encoder knowledge distillation for industrial anomaly detection [JMS 2024]
- Masked feature regeneration based asymmetric student–teacher network for anomaly detection [Multimedia Tools and Applications 2024]
- Feature-Constrained and Attention-Conditioned Distillation Learning for Visual Anomaly Detection [ICASSP 2024]
- Patch svdd: Patch-level svdd for anomaly detection and segmentation [ACCV 2020]
- Anomaly detection using improved deep SVDD model with data structure preservation [2021]
- A Semantic-Enhanced Method Based On Deep SVDD for Pixel-Wise Anomaly Detection [2021]
- MOCCA: Multilayer One-Class Classification for Anomaly Detection [2021]
- Defect Detection of Metal Nuts Applying Convolutional Neural Networks [2021]
- Panda: Adapting pretrained features for anomaly detection and segmentation [2021]
- Mean-shifted contrastive loss for anomaly detection [2021]
- Learning and Evaluating Representations for Deep One-Class Classification [2020]
- Self-supervised learning for anomaly detection with dynamic local augmentation [2021]
- Contrastive Predictive Coding for Anomaly Detection [2021]
- Cutpaste: Self-supervised learning for anomaly detection and localization [ICCV 2021][unofficial code]
- Consistent estimation of the max-flow problem: Towards unsupervised image segmentation [2020]
- MemSeg: A semi-supervised method for image surface defect detection using differences and commonalities [2022][unofficial code]
- SimpleNet: A Simple Network for Image Anomaly Detection and Localization [CVPR 2023][code]
- End-to-End Augmentation Hyperparameter Tuning for Self-Supervised Anomaly Detection [2023]
- Anomaly Detection under Distribution Shift [ICCV 2023][code]
- Learning Transferable Representations for Image Anomaly Localization Using Dense Pretraining [WACV 2024][code]
- Anomaly Detection in Nanofibrous Materials by CNN-Based Self-Similarity [Sensors 2018]
- A Multi-Scale A Contrario method for Unsupervised Image Anomaly Detection [2021]
- Modeling the distribution of normal data in pre-trained deep features for anomaly detection [2021]
- Transfer Learning Gaussian Anomaly Detection by Fine-Tuning Representations [2021]
- PEDENet: Image anomaly localization via patch embedding and density estimation [2022]
- Unsupervised image anomaly detection and segmentation based on pre-trained feature mapping [2022]
- Position Encoding Enhanced Feature Mapping for Image Anomaly Detection [2022][code]
- Focus your distribution: Coarse-to-fine non-contrastive learning for anomaly detection and localization [ICME 2022]
- Anomaly Detection of Defect using Energy of Point Pattern Features within Random Finite Set Framework [2021][code]
- Fastflow: Unsupervised anomaly detection and localization via 2d normalizing flows [2021][unofficial code]
- Same same but differnet: Semi-supervised defect detection with normalizing flows [WACV 2021][code]
- Fully convolutional cross-scale-flows for image-based defect detection [WACV 2022][code]
- Cflow-ad: Real-time unsupervised anomaly detection with localization via conditional normalizing flows [WACV 2022][code]
- CAINNFlow: Convolutional block Attention modules and Invertible Neural Networks Flow for anomaly detection and localization tasks [2022]
- AltUB: Alternating Training Method to Update Base Distribution of Normalizing Flow for Anomaly Detection [2022]
- Collaborative Discrepancy Optimization for Reliable Image Anomaly Localization [TII 2023][code]
- PyramidFlow: High-Resolution Defect Contrastive Localization using Pyramid Normalizing Flow [CVPR 2023][code]
- Attention Modules Improve Image-Level Anomaly Detection for Industrial Inspection: A DifferNet Case Study [WACV 2024]
- Fascinating Supervisory Signals and Where to Find Them: Deep Anomaly Detection with Scale Learning [ICML 2023]
- FRAnomaly: flow-based rapid anomaly detection from images [Applied Intelligence 2024]
- Image alignment-based patch distribution framework for anomaly detection [ICCVDM 2024]
- ReConPatch: Contrastive Patch Representation Learning for Industrial Anomaly Detection [WACV 2024]
- Sub-image anomaly detection with deep pyramid correspondences [2020]
- Semi-orthogonal embedding for efficient unsupervised anomaly segmentation [2021]
- Anomaly Detection Via Self-Organizing Map [2021]
- PaDiM: A Patch Distribution Modeling Framework for Anomaly Detection and Localization [ICPR 2021][unofficial code]
- Industrial Image Anomaly Localization Based on Gaussian Clustering of Pretrained Feature [2021]
- Towards total recall in industrial anomaly detection[CVPR 2022][code]
- CFA: Coupled-Hypersphere-Based Feature Adaptation for Target-Oriented Anomaly Localization[2022][code]
- FAPM: Fast Adaptive Patch Memory for Real-time Industrial Anomaly Detection[2022]
- N-pad: Neighboring Pixel-based Industrial Anomaly Detection [2022]
- Multi-scale patch-based representation learning for image anomaly detection and segmentation [2022]
- SPot-the-Difference Self-supervised Pre-training for Anomaly Detection and Segmentation [ECCV 2022]
- Diversity-Measurable Anomaly Detection [CVPR 2023]
- SelFormaly: Towards Task-Agnostic Unified Anomaly Detection[2023]
- REB: Reducing Biases in Representation for Industrial Anomaly Detection [2023][code]
- PNI : Industrial Anomaly Detection using Position and Neighborhood Information [ICCV 2023][code]
- Inter-Realization Channels: Unsupervised Anomaly Detection Beyond One-Class Classification [ICCV 2023][code]
- Grid-Based Continuous Normal Representation for Anomaly Detection [2024][code]
- PointCore: Efficient Unsupervised Point Cloud Anomaly Detector Using Local-Global Features [2024]
- DMAD: Dual Memory Bank for Real-World Anomaly Detection [2024]
- A Reconstruction-Based Feature Adaptation for Anomaly Detection with Self-Supervised Multi-Scale Aggregation [ICASSP 2024]
- Random Word Data Augmentation with CLIP for Zero-Shot Anomaly Detection [BMVC 2023]
- AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detection [ICLR 2024][code]
- WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation [CVPR 2023]
- ClipSAM: CLIP and SAM Collaboration for Zero-Shot Anomaly Segmentation [2023]
- CLIP-AD: A Language-Guided Staged Dual-Path Model for Zero-shot Anomaly Detection [2023]
- AnoVL: Adapting Vision-Language Models for Unified Zero-shot Anomaly Localization [2023][code]
- AnomalyGPT: Detecting Industrial Anomalies using Large Vision-Language Models [AAAI 2024][code][project page]
- Anomaly Detection by Adapting a pre-trained Vision Language Model [2024]
- Customizing Visual-Language Foundation Models for Multi-modal Anomaly Detection and Reasoning [2024][code]
- PromptAD: Learning Prompts with only Normal Samples for Few-Shot Anomaly Detection [CVPR 2024][code]
- Do LLMs Understand Visual Anomalies? Uncovering LLM Capabilities in Zero-shot Anomaly Detection [2024]
- Improving unsupervised defect segmentation by applying structural similarity to autoencoders [2018]
- Automatic Fabric Defect Detection with a Multi-Scale Convolutional Denoising Autoencoder Network Model [Sensors 2018]
- An Unsupervised-Learning-Based Approach for Automated Defect Inspection on Textured Surfaces [TIM 2018]
- Unsupervised anomaly detection using style distillation [2020]
- Unsupervised two-stage anomaly detection [2021]
- Dfr: Deep feature reconstruction for unsupervised anomaly segmentation [Neurocomputing 2020]
- Unsupervised anomaly segmentation via multilevel image reconstruction and adaptive attention-level transition [2021]
- Encoding structure-texture relation with p-net for anomaly detection in retinal images [2020]
- Improved anomaly detection by training an autoencoder with skip connections on images corrupted with stain-shaped noise [2021]
- Unsupervised anomaly detection for surface defects with dual-siamese network [2022]
- Divide-and-assemble: Learning block-wise memory for unsupervised anomaly detection [ICCV 2021]
- Reconstruction from edge image combined with color and gradient difference for industrial surface anomaly detection [2022][code]
- Spatial Contrastive Learning for Anomaly Detection and Localization [2022]
- Superpixel masking and inpainting for self-supervised anomaly detection [BMVC 2020]
- Iterative image inpainting with structural similarity mask for anomaly detection [2020]
- Self-Supervised Masking for Unsupervised Anomaly Detection and Localization [2022]
- Reconstruction by inpainting for visual anomaly detection [PR 2021]
- Draem-a discriminatively trained reconstruction embedding for surface anomaly detection [ICCV 2021][code]
- DSR: A dual subspace re-projection network for surface anomaly detection [ECCV 2022][code]
- Natural Synthetic Anomalies for Self-supervised Anomaly Detection and Localization [ECCV 2022][code]
- Self-Supervised Training with Autoencoders for Visual Anomaly Detection [2022]
- Self-supervised predictive convolutional attentive block for anomaly detection [CVPR 2022 oral][code]
- Self-Supervised Masked Convolutional Transformer Block for Anomaly Detection [TPAMI 2022][code]
- Iterative energy-based projection on a normal data manifold for anomaly localization [2019]
- Towards visually explaining variational autoencoders [2020]
- Deep generative model using unregularized score for anomaly detection with heterogeneous complexity [2020]
- Anomaly localization by modeling perceptual features [2020]
- Image anomaly detection using normal data only by latent space resampling [2020]
- Noise-to-Norm Reconstruction for Industrial Anomaly Detection and Localization [2023]
- Patch-wise Auto-Encoder for Visual Anomaly Detection [2023]
- FAIR: Frequency-aware Image Restoration for Industrial Visual Anomaly Detection [2023][code comming soon]
- Template-guided Hierarchical Feature Restoration for Anomaly Detection [ICCV 2023]
- FastRecon: Few-shot Industrial Anomaly Detection via Fast Feature Reconstruction [ICCV 2023][code comming soon]
- Produce Once, Utilize Twice for Anomaly Detection [2023]
- RealNet: A Feature Selection Network with Realistic Synthetic Anomaly for Anomaly Detection [CVPR 2024][code]
- Implicit Foreground-Guided Network for Anomaly Detection and Localization [ICASSP 2024]
- Neural Network Training Strategy To Enhance Anomaly Detection Performance: A Perspective On Reconstruction Loss Amplification [ICASSP 2024]
- Patch-Wise Augmentation for Anomaly Detection and Localization [ICASSP 2024]
- A Reconstruction-Based Feature Adaptation for Anomaly Detection with Self-Supervised Multi-Scale Aggregation [ICASSP 2024]
- Mixed-Attention Auto Encoder for Multi-Class Industrial Anomaly Detection [ICASSP 2024]
- Dual-Constraint Autoencoder and Adaptive Weighted Similarity Spatial Attention for Unsupervised Anomaly Detection [TII 2024]
- Omni-frequency Channel-selection Representations for Unsupervised Anomaly Detection [TIP 2023][code]
- Learning semantic context from normal samples for unsupervised anomaly detection [AAAI 2021]
- Anoseg: Anomaly segmentation network using self-supervised learning [2021]
- A Surface Defect Detection Method Based on Positive Samples [PRICAI 2018]
- Few-shot defect image generation via defect-aware feature manipulation [AAAI 2023][code]
- VT-ADL: A vision transformer network for image anomaly detection and localization [ISIE 2021]
- ADTR: Anomaly Detection Transformer with Feature Reconstruction [2022]
- AnoViT: Unsupervised Anomaly Detection and Localization With Vision Transformer-Based Encoder-Decoder [2022]
- HaloAE: An HaloNet based Local Transformer Auto-Encoder for Anomaly Detection and Localization [2022]
- Inpainting transformer for anomaly detection [ICIAP 2022]
- Masked Swin Transformer Unet for Industrial Anomaly Detection [2022]
- Masked Transformer for image Anomaly Localization [TII 2022]
- Focus the Discrepancy: Intra- and Inter-Correlation Learning for Image Anomaly Detection [ICCV 2023][code]
- AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and Localization [TASE 2024]
- AnoDDPM: Anomaly Detection With Denoising Diffusion Probabilistic Models Using Simplex Noise [CVPR Workshop 2022]
- Unsupervised Visual Defect Detection with Score-Based Generative Model[2022]
- DiffusionAD: Denoising Diffusion for Anomaly Detection [2023][code]
- Anomaly Detection with Conditioned Denoising Diffusion Models [2023]
- Unsupervised Surface Anomaly Detection with Diffusion Probabilistic Model [ICCV 2023]
- Removing Anomalies as Noises for Industrial Defect Localization [ICCV 2023]
- TransFusion -- A Transparency-Based Diffusion Model for Anomaly Detection [2023]
- LafitE: Latent Diffusion Model with Feature Editing for Unsupervised Multi-class Anomaly Detection [2023]
- DiAD: A Diffusion-based Framework for Multi-class Anomaly Detection [AAAI 2024][code]
- D3AD: Dynamic Denoising Diffusion Probabilistic Model for Anomaly Detection [2024]
- Anomaly Detection using Score-based Perturbation Resilience [ICCV 2023]
- Neural batch sampling with reinforcement learning for semi-supervised anomaly detection [ECCV 2020]
- Explainable Deep One-Class Classification [ICLR 2020]
- Attention guided anomaly localization in images [ECCV 2020]
- Mixed supervision for surface-defect detection: From weakly to fully supervised learning [2021]
- Explainable deep few-shot anomaly detection with deviation networks [2021][code]
- Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detection [CVPR 2022][code]
- Anomaly Clustering: Grouping Images into Coherent Clusters of Anomaly Types[WACV 2023]
- Prototypical Residual Networks for Anomaly Detection and Localization [CVPR 2023][code]
- Efficient Anomaly Detection with Budget Annotation Using Semi-Supervised Residual Transformer [2023]
- Anomaly Heterogeneity Learning for Open-set Supervised Anomaly Detection [CVPR 2024][code]
- Few-shot defect image generation via defect-aware feature manipulation [AAAI 2023][code]
- AnomalyDiffusion: Few-Shot Anomaly Image Generation with Diffusion Model [AAAI 2024][code]
- BiaS: Incorporating Biased Knowledge to Boost Unsupervised Image Anomaly Localization [TSMC 2024]
- DMAD: Dual Memory Bank for Real-World Anomaly Detection [2024]
- Logit Inducing With Abnormality Capturing for Semi-Supervised Image Anomaly Detection [2022]
- An effective framework of automated visual surface defect detection for metal parts [2021]
- Interleaved Deep Artifacts-Aware Attention Mechanism for Concrete Structural Defect Classification [TIP 2021]
- Reference-based defect detection network [TIP 2021]
- Fabric defect detection using tactile information [ICRA 2021]
- A lightweight spatial and temporal multi-feature fusion network for defect detection [TIP 2020]
- SDD-CNN: Small Data-Driven Convolution Neural Networks for Subtle Roller Defect Inspection [Robotics and Computer-Integrated Manufacturing 2020]
- A High-Efficiency Fully Convolutional Networks for Pixel-Wise Surface Defect Detection [IEEE Access 2019]
- SDD-CNN: Small Data-Driven Convolution Neural Networks for Subtle Roller Defect Inspection [Applied Sciences 2019]
- Autonomous Structural Visual Inspection Using Region-Based Deep Learning for Detecting Multiple Damage Types [CACIE 2018]
- Detection and segmentation of manufacturing defects with convolutional neural networks and transfer learning [2018]
- Automatic Metallic Surface Defect Detection and Recognition with Convolutional Neural Networks [Applied Sciences 2018]
- Real-time Detection of Steel Strip Surface Defects Based on Improved YOLO Detection Network [IFAC-PapersOnLine 2018]
- Domain adaptation for automatic OLED panel defect detection using adaptive support vector data description [IJCV 2017]
- Automatic Defect Detection of Fasteners on the Catenary Support Device Using Deep Convolutional Neural Network [TIM 2017]
- Deep Active Learning for Civil Infrastructure Defect Detection and Classification Computing in civil engineering 2017
- A fast and robust convolutional neural network-based defect detection model in product quality control [IJAMT 2017]
- Defects Detection Based on Deep Learning and Transfer Learning [Metallurgical & Mining Industry 2015]
- Design of deep convolutional neural network architectures for automated feature extraction in industrial inspection [CIRP annals 2016]
- Decision Fusion Network with Perception Fine-tuning for Defect Classification [2023]
- Global Context Aggregation Network for Lightweight Saliency Detection of Surface Defects [2023]
- Dual Attention U-Net with Feature Infusion: Pushing the Boundaries of Multiclass Defect Segmentation [2023][code]
- Learning unsupervised metaformer for anomaly detection [ICCV 2021]
- Registration based few-shot anomaly detection [ECCV 2022 oral][code]
- Same same but differnet: Semi-supervised defect detection with normalizing flows [(Distribution)WACV 2021]
- Towards total recall in industrial anomaly detection [(Memory bank)CVPR 2022]
- A hierarchical transformation-discriminating generative model for few shot anomaly detection [ICCV 2021]
- Anomaly detection of defect using energy of point pattern features within random finite set framework [2021]
- Optimizing PatchCore for Few/many-shot Anomaly Detection [2023][code]
- AnomalyGPT: Detecting Industrial Anomalies using Large Vision-Language Models [AAAI 2024][code][project page]
- FastRecon: Few-shot Industrial Anomaly Detection via Fast Feature Reconstruction [ICCV 2023][code comming soon]
- Produce Once, Utilize Twice for Anomaly Detection [2023]
- COFT-AD: COntrastive Fine-Tuning for Few-Shot Anomaly Detection [TIP2024]
- Text-Guided Variational Image Generation for Industrial Anomaly Detection and Segmentation [CVPR 2024]
- Multimodal Industrial Anomaly Detection by Crossmodal Feature Mapping [CVPR 2024]
- Dual-path Frequency Discriminators for Few-shot Anomaly Detection [2024]
- Few-shot Online Anomaly Detection and Segmentation [2024]
- Random Word Data Augmentation with CLIP for Zero-Shot Anomaly Detection [BMVC 2023]
- Zero-Shot Batch-Level Anomaly Detection [2023]
- Zero-shot versus Many-shot: Unsupervised Texture Anomaly Detection [WACV 2023]
- MAEDAY: MAE for few and zero shot AnomalY-Detection [2022]
- WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation [CVPR 2023]
- Segment Any Anomaly without Training via Hybrid Prompt Regularization [2023] [code]
- Anomaly Detection in an Open World by a Neuro-symbolic Program on Zero-shot Symbols [IROS 2022 Workshop]
- 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]
- AnoVL: Adapting Vision-Language Models for Unified Zero-shot Anomaly Localization [2023][code]
- CLIP-AD: A Language-Guided Staged Dual-Path Model for Zero-shot Anomaly Detection [2023]
- PromptAD: Zero-shot Anomaly Detection using Text Prompts [WACV 2024]
- High-Fidelity Zero-Shot Texture Anomaly Localization Using Feature Correspondence Analysis [WACV 2024]
- AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detection [ICLR 2024][code]
- MuSc: Zero-Shot Anomaly Classification and Segmentation by Mutual Scoring of the Unlabeled Images[ICLR 2024][code]
- ClipSAM: CLIP and SAM Collaboration for Zero-Shot Anomaly Segmentation [2023]
- APRIL-GAN: A Zero-/Few-Shot Anomaly Classification and Segmentation Method for CVPR 2023 VAND Workshop Challenge Tracks 1&2: 1st Place on Zero-shot AD and 4th Place on Few-shot AD [2023]
- Model Selection of Zero-shot Anomaly Detectors in the Absence of Labeled Validation Data [2024]
- Toward Generalist Anomaly Detection via In-context Residual Learning with Few-shot Sample Prompts [CVPR 2024][code]+ PromptAD: Learning Prompts with only Normal Samples for Few-Shot Anomaly Detection [CVPR 2024][code]
- Do LLMs Understand Visual Anomalies? Uncovering LLM Capabilities in Zero-shot Anomaly Detection [2024]
- Trustmae: A noise-resilient defect classification framework using memory-augmented auto-encoders with trust regions [WACV 2021]
- Self-Supervise, Refine, Repeat: Improving Unsupervised Anomaly Detection [TMLR 2021]
- Data refinement for fully unsupervised visual inspection using pre-trained networks [2022]
- Latent Outlier Exposure for Anomaly Detection with Contaminated Data [ICML 2022]
- Deep one-class classification via interpolated gaussian descriptor [AAAI 2022 oral][code]
- SoftPatch: Unsupervised Anomaly Detection with Noisy Data [NeurIPS 2022])[code]
- Inter-Realization Channels: Unsupervised Anomaly Detection Beyond One-Class Classification [ICCV 2023][code]
- Cutpaste: Self-supervised learning for anomaly detection and localization [(OCC)ICCV 2021][unofficial code]
- Draem-a discriminatively trained reconstruction embedding for surface anomaly detection [(Reconstruction AE)ICCV 2021][code]
- MemSeg: A semi-supervised method for image surface defect detection using differences and commonalities [(OCC)2022][unofficial code]
- A High-Efficiency Fully Convolutional Networks for Pixel-Wise Surface Defect Detection [IEEE Access 2019]
- Multistage GAN for fabric defect detection [2019]
- Gan-based defect synthesis for anomaly detection in fabrics [2020]
- Defect image sample generation with GAN for improving defect recognition [2020]
- Defective samples simulation through neural style transfer for automatic surface defect segment [2020]
- A simulation-based few samples learning method for surface defect segmentation [2020]
- Synthetic data augmentation for surface defect detection and classification using deep learning [2020]
- Defect Transfer GAN: Diverse Defect Synthesis for Data Augmentation [BMVC 2022]
- Defect-GAN: High-fidelity defect synthesis for automated defect inspection [2021]
- EID-GAN: Generative Adversarial Nets for Extremely Imbalanced Data Augmentation[TII 2022]
- Multilevel Saliency-Guided Self-Supervised Learning for Image Anomaly Detection [2023]
- AnomalyDiffusion: Few-Shot Anomaly Image Generation with Diffusion Model [AAAI 2024][code]
- RealNet: A Feature Selection Network with Realistic Synthetic Anomaly for Anomaly Detection [CVPR 2024][code]
- Dual-path Frequency Discriminators for Few-shot Anomaly Detection [2024]
- A Novel Approach to Industrial Defect Generation through Blended Latent Diffusion Model with Online Adaptation [2024][code]
- A Comprehensive Augmentation Framework for Anomaly Detection [AAAI 2024]
- CAGEN: Controllable Anomaly Generator using Diffusion Model [ICASSP 2024]
- Anomaly detection in 3d point clouds using deep geometric descriptors [WACV 2022]
- Back to the feature: classical 3d features are (almost) all you need for 3D anomaly detection [2022][code]
- Anomaly Detection Requires Better Representations [2022]
- Asymmetric Student-Teacher Networks for Industrial Anomaly Detection [WACV 2022]
- Multimodal Industrial Anomaly Detection via Hybrid Fusion [CVPR 2023]
- Complementary Pseudo Multimodal Feature for Point Cloud Anomaly Detection [2023][code]
- Image-Pointcloud Fusion based Anomaly Detection using PD-REAL Dataset [2023][data]
- Towards Scalable 3D Anomaly Detection and Localization: A Benchmark via 3D Anomaly Synthesis and A Self-Supervised Learning Network [CVPR 2024][code]
- Shape-Guided Dual-Memory Learning for 3D Anomaly Detection [ICML 2023]
- EasyNet: An Easy Network for 3D Industrial Anomaly Detection [ACM MM 2023]
- Real3D-AD: A Dataset of Point Cloud Anomaly Detection [NeurIPS 2023][data]
- Self-supervised Feature Adaptation for 3D Industrial Anomaly Detection [2024]
- Cheating Depth: Enhancing 3D Surface Anomaly Detection via Depth Simulation [WACV 2024][code]
- Incremental Template Neighborhood Matching for 3D anomaly detection [Neurocomputing 2024]
- Keep DRÆMing: Discriminative 3D anomaly detection through anomaly simulation [PRL 2024]
- Rethinking Reverse Distillation for Multi-Modal Anomaly Detection [AAAI 2024]
- Multimodal Industrial Anomaly Detection by Crossmodal Feature Mapping [CVPR 2024]
- PointCore: Efficient Unsupervised Point Cloud Anomaly Detector Using Local-Global Features [2024]
- Towards Total Online Unsupervised Anomaly Detection and Localization in Industrial Vision [2023]
- Towards Continual Adaptation in Industrial Anomaly Detection [ACM MM 2022]
- An Incremental Unified Framework for Small Defect Inspection [2023][code]
- Unsupervised Continual Anomaly Detection with Contrastively-learned Prompt [AAAI 2024][code]
- A Unified Model for Multi-class Anomaly Detection [NeurIPS 2022] [code]
- OmniAL A unifiled CNN framework for unsupervised anomaly localization [CVPR 2023]
- SelFormaly: Towards Task-Agnostic Unified Anomaly Detection[2023]
- Hierarchical Vector Quantized Transformer for Multi-class Unsupervised Anomaly Detection [NeurIPS 2023][code]
- Removing Anomalies as Noises for Industrial Defect Localization [ICCV 2023]
- UniFormaly: Towards Task-Agnostic Unified Framework for Visual Anomaly Detection [2023][code]
- MSTAD: A masked subspace-like transformer for multi-class anomaly detection [2023]
- LafitE: Latent Diffusion Model with Feature Editing for Unsupervised Multi-class Anomaly Detection [2023]
- DiAD: A Diffusion-based Framework for Multi-class Anomaly Detection [AAAI 2024][code]
- Exploring Plain ViT Reconstruction for Multi-class Unsupervised Anomaly Detection [2023]
- Structural Teacher-Student Normality Learning for Multi-Class Anomaly Detection and Localization [2024]
- Unsupervised anomaly detection and localization with one model for all category [KBS 2024]
- Anomaly Detection by Adapting a pre-trained Vision Language Model [2024]
- DMAD: Dual Memory Bank for Real-World Anomaly Detection [2024]
- Toward Multi-class Anomaly Detection: Exploring Class-aware Unified Model against Inter-class Interference [2024]
- Hierarchical Gaussian Mixture Normalizing Flow Modeling for Unified Anomaly Detection [2024]
- Long-Tailed Anomaly Detection with Learnable Class Names [CVPR 2024][data split]
- MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection [2024][code]
- Learning Feature Inversion for Multi-class Anomaly Detection under General-purpose COCO-AD Benchmark [2024][code]
- Beyond Dents and Scratches: Logical Constraints in Unsupervised Anomaly Detection and Localization [IJCV 2022]
- Set Features for Fine-grained Anomaly Detection[2023] [code]
- SLSG: Industrial Image Anomaly Detection by Learning Better Feature Embeddings and One-Class Classification [2023]
- EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies [WACV 2024]
- Contextual Affinity Distillation for Image Anomaly Detection [WACV 2024]
- REB: Reducing Biases in Representation for Industrial Anomaly Detection [2023][code]
- Learning Global-Local Correspondence with Semantic Bottleneck for Logical Anomaly Detection [TCSVT 2023][code]
- Template-guided Hierarchical Feature Restoration for Anomaly Detection [ICCV 2023]
- Few Shot Part Segmentation Reveals Compositional Logic for Industrial Anomaly Detection [AAAI 2024]
- Generating and Reweighting Dense Contrastive Patterns for Unsupervised Anomaly Detection [AAAI 2024]
- PUAD: Frustratingly Simple Method for Robust Anomaly Detection [2024]
Test Time Training for Industrial Anomaly Segmentation [2024]
| Dataset | Class | Normal | Abnormal | Total | Annotation level | Source | Time |
|---|---|---|---|---|---|---|---|
| AITEX | 1 | 140 | 105 | 245 | Segmentation mask | RGB real | 2019 |
| Anomaly-ShapeNet[data] | 40 | - | - | 1600 | Point-level mask | Point-cloud synthetic | CVPR,2024 |
| BTAD | 3 | - | - | 2830 | Segmentation mask | RGB real | 2021 |
| CID | 1 | 4060 | 233 | 4293 | Segmentation mask | RGB real | 2024,TIM |
| DAGM | 10 | - | - | 11500 | Segmentation mask | RGB synthetic | 2007 |
| DEEPPCB | 1 | - | - | 1500 | Bounding box | RGB synthetic | 2019 |
| DTD-Synthetic | 12 | - | - | - | Segmentation mask | RGB synthetic | WACV,2024 |
| Eyecandies | 10 | 13250 | 2250 | 15500 | Segmentation mask | RGBD synthetic image | ACCV,2022 |
| Fabirc dataset | 1 | 25 | 25 | 50 | Segmentation mask | RGB synthetic | PR,2016 |
| GDXray | 1 | 0 | 19407 | 19407 | Bounding box | RGB real | 2016 |
| KolekrotSDD | 1 | 347 | 52 | 399 | Segmentation mask | RGB real | JIM,2019 |
| KolekrotSDD2 | 1 | 2979 | 356 | 3335 | Segmentation mask | RGB real | CiI,2021 |
| MIAD | 7 | 87500 | 17500 | 105000 | Segmentation mask | RGB synthetic | 2023 |
| MPDD | 6 | 1064 | 282 | 1346 | Segmentation mask | RGB real | ICUMT,2021 |
| MTD | 1 | 952 | 392 | 1344 | Segmentation mask | RGB real | CASE,2018 |
| MVTec AD | 15 | 4096 | 1258 | 5354 | Segmentation mask | RGB real | CVPR,2019 |
| MVTec 3D-AD | 10 | 2904 | 948 | 3852 | Segmentation mask | RGB real | VISAPP,2021 |
| MVTec LOCO-AD | 5 | 2347 | 993 | 3340 | Segmentation mask | RGBD real | IJCV,2022 |
| NanoTwice | 1 | 5 | 40 | 45 | Segmentation mask | RGB real | TII,2016 |
| NEU surface defect | 1 | 0 | 1800 | 1800 | Bounding box | RGB real | 2013 |
| PAD | 20 | 5231 | 4902 | 10133 | Segmentation mask | RBG synthetic | NeurIPS,2023 |
| Real-IAD | 30 | 99721 | 51329 | 151050 | Segmentation mask | RGB real | CVPR,2024 |
| Real3D-AD | 12 | 652 | 602 | 1254 | Point-level mask | Point-cloud real | NeurIPS,2023 |
| RSDD | 2 | - | - | 195 | Segmentation mask | RGB real | 2017 |
| Steel defect detection | 1 | - | - | 18076 | Image | RGB real | 2019 |
| Steel tube dataset | 1 | 0 | 3408 | 3408 | Bounding box | RGB real | 2021 |
| VisA | 12 | 9621 | 1200 | 10821 | Segmentation mask | RGB real | ECCV,2022 |
- (NEU surface defect dataset)A noise robust method based on completed local binary patterns for hot-rolled steel strip surface defects [2013] [data]
- (Steel tube dataset)Deep learning based steel pipe weld defect detection [2021] [data]
- (Steel defect dataset)Severstal: Steel Defect Detection [data 2019]
- (NanoTwice)Defect detection in SEM images of nanofibrous materials [TII 2016] [data]
- (GDXray)GDXray: The database of X-ray images for nondestructive testing [2015] [data]
- (DEEP PCB)Online PCB defect detector on a new PCB defect dataset [2019] [data]
- (Fabric dataset)Fabric inspection based on the Elo rating method [PR 2016]
- (KolektorSDD)Segmentation-based deep-learning approach for surface-defect detection [Journal of Intelligent Manufacturing] [data]
- (KolektorSDD2)Mixed supervision for surface-defect detection: From weakly to fully supervised learning [Computers in Industry 2021] [data]
- (RSDD)A hierarchical extractor-based visual rail surface inspection system [2017]
- (Eyecandies)The Eyecandies Dataset for Unsupervised Multimodal Anomaly Detection and Localization [ACCV 2022] [data]
- (MVTec AD)MVTec AD: A comprehensive real-world dataset for unsupervised anomaly detection [CVPR 2019] [IJCV 2021] [data]
- (MVTec 3D-AD)The mvtec 3d-ad dataset for unsupervised 3d anomaly detection and localization [VISAPP 2021] [data]
- (MVTec LOCO-AD)Beyond Dents and Scratches: Logical Constraints in Unsupervised Anomaly Detection and Localization [IJCV 2022] [data]
- (MPDD)Deep learning-based defect detection of metal parts: evaluating current methods in complex conditions [ICUMT 2021] [data]
- (BTAD)VT-ADL: A vision transformer network for image anomaly detection and localization [2021] [data]
- (VisA)SPot-the-Difference Self-supervised Pre-training for Anomaly Detection and Segmentation [ECCV 2022] [data]
- (MTD)Surface defect saliency of magnetic tile [2020] [data]
- (DAGM)DAGM dataset [data 2007]
- (MIAD)Miad:A maintenance inspection dataset for unsupervised anomaly detection [2022] [data]
- CVPR 1st workshop on Vision-based InduStrial InspectiON [homepage] [data]
- (SSGD)SSGD: A smartphone screen glass dataset for defect detection [2023][dataset is coming soon]
- (AeBAD)Industrial Anomaly Detection with Domain Shift: A Real-world Dataset and Masked Multi-scale Reconstruction [2023] [data]
- VISION Datasets: A Benchmark for Vision-based InduStrial InspectiON [2023] [data]
- PAD: A Dataset and Benchmark for Pose-agnostic Anomaly Detection [NeurIPS 2023]
- PKU-GoodsAD: A Supermarket Goods Dataset for Unsupervised Anomaly Detection and Segmentation [2023][data]
- Real3D-AD: A Dataset of Point Cloud Anomaly Detection [NeurIPS 2023][data]
- InsPLAD: A Dataset and Benchmark for Power Line Asset Inspection in UAV Images [IJRS 2023][data]
- Image-Pointcloud Fusion based Anomaly Detection using PD-REAL Dataset [2023][data]
- CrashCar101: Procedural Generation for Damage Assessment [WACV 2024][data]
- Defect Spectrum: A Granular Look of Large-Scale Defect Datasets with Rich Semantics [2023][data]
- (DTD-Synthetic) Zero-shot versus Many-shot: Unsupervised Texture Anomaly Detection [WACV 2023][data]
- Towards Scalable 3D Anomaly Detection and Localization: A Benchmark via 3D Anomaly Synthesis and A Self-Supervised Learning Network [CVPR 2024][data]
- Real-IAD: A Real-World Multi-view Dataset for Benchmarking Versatile Industrial Anomaly Detection [CVPR 2024][code][data]
- Catenary Insulator Defects Detection: A Dataset and an Unsupervised Baseline [TIM 2024][code]
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}
}