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Self-Supervised Masking for Unsupervised Anomaly Detection and Localization

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A Hierarchical Transformation-Discriminating Generative Model for Few Shot Anomaly Detection

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Learning Unsupervised Metaformer for Anomaly Detection

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Focus Your Distribution: Coarse-to-Fine Non-Contrastive Learning for Anomaly Detection and Localization

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WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation

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Deep Industrial Image Anomaly Detection: A Survey

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AnomalyGPT: Detecting Industrial Anomalies Using Large Vision-Language Models

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A Diffusion-Based Framework for Multi-Class Anomaly Detection

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AnomalyDiffusion: Few-Shot Anomaly Image Generation with Diffusion Model

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AdaCLIP: Adapting CLIP with Hybrid Learnable Prompts for Zero-Shot Anomaly Detection

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Real-IAD: A Real-World Multi-View Dataset for Benchmarking Versatile Industrial Anomaly Detection

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PromptAD: Learning Prompts with only Normal Samples for Few-Shot Anomaly Detection

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Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detection

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Adapting Visual-Language Models for Generalizable Anomaly Detection in Medical Images

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Diversity-Measurable Anomaly Detection

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FastRecon: Few-shot Industrial Anomaly Detection via Fast Feature Reconstruction

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IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturing

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OmniAL: A Unified CNN Framework for Unsupervised Anomaly Localization

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CLIP-AD: A Language-Guided Staged Dual-Path Model for Zero-Shot Anomaly Detection

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Toward Generalist Anomaly Detection via In-Context Residual Learning with Few-Shot Sample Prompts

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MAEDAY: MAE for few- and zero-shot AnomalY-Detection

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MedIAnomaly: A comparative study of anomaly detection in medical images

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Personalizing Vision-Language Models With Hybrid Prompts for Zero-Shot Anomaly Detection

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Template-guided Hierarchical Feature Restoration for Anomaly Detection

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AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and Localization

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PromptAD: Zero-shot Anomaly Detection using Text Prompts

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MediCLIP: Adapting CLIP for Few-Shot Medical Image Anomaly Detection

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Target Before Shooting: Accurate Anomaly Detection and Localization Under One Millisecond via Cascade Patch Retrieval

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FewSOME: One-Class Few Shot Anomaly Detection with Siamese Networks

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A survey on RGB, 3D, and multimodal approaches for unsupervised industrial image anomaly detection

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One-for-All: Proposal Masked Cross-Class Anomaly Detection

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COFT-AD: COntrastive Fine-Tuning for Few-Shot Anomaly Detection

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PKU-GoodsAD: A Supermarket Goods Dataset for Unsupervised Anomaly Detection and Segmentation

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FR-PatchCore: An Industrial Anomaly Detection Method for Improving Generalization

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Small Object Few-Shot Segmentation for Vision-Based Industrial Inspection

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Low-Shot Unsupervised Visual Anomaly Detection via Sparse Feature Representation

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Unsupervised anomaly detection and localization with one model for all category

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Prototypical Learning Guided Context-Aware Segmentation Network for Few-Shot Anomaly Detection

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Foundation models and Transformers for anomaly detection: A survey

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Few-Shot Anomaly Detection via Category-Agnostic Registration Learning

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AF-CLIP: Zero-Shot Anomaly Detection via Anomaly-Focused CLIP Adaptation

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What Makes a Good Data Augmentation for Few-Shot Unsupervised Image Anomaly Detection?

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Few-shot online anomaly detection and segmentation

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Produce Once, Utilize Twice for Anomaly Detection

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Hierarchical-Instance Contrastive Learning for Minority Detection on Imbalanced Medical Datasets

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Dual-path Frequency Discriminators for few-shot anomaly detection

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Long-Tailed Anomaly Detection with Learnable Class Names

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DiffUT: Diffusion-based augmentation for limited ultrasonic testing defects in high-speed rail

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MoEAD: A Parameter-Efficient Model for Multi-class Anomaly Detection

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A survey on industrial image anomaly detection: methods, benchmarks and rethinks

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Self-Supervised Surface Defect Localization via Joint De-Anomaly Reconstruction and Saliency-Guided Segmentation

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Deep feature clustering for multi-class industrial image anomaly detection

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Zero-Shot Industrial Anomaly Detection via CLIP-DINOv2 Multimodal Fusion and Stabilized Attention Pooling

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Advancing unsupervised anomaly detection with normalizing flow and multi-scale ensemble learning

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DMMGNet: A discrimination mapping and memory bank mean guidance-based network for high-performance few-shot industrial anomaly detection

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That’s BAD: blind anomaly detection by implicit local feature clustering

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SimCLIP: Refining Image-Text Alignment with Simple Prompts for Zero-/Few-shot Anomaly Detection

Deng, C., Xu, H., Chen, X., Xu, H., Tu, X., Ding, X., & Huang, Y. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3681376 · Cited by 12 · see citing works

Reconstruction-based visual anomaly detection in wound rotor synchronous machine production using convolutional autoencoders and structural similarity

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G-Anomaly: A Pyramid Graph Transformer-Based Vision-Language Model for General Industrial Anomaly Detection

Li, J., Wen, S., & Fang, B. (2025) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2025.3552986 · Cited by 11 · see citing works

A feature matching-based method for few-shot multivariate time series anomaly detection with symmetric patch mask Siam Transformer

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High-Fidelity Zero-Shot Texture Anomaly Localization Using Feature Correspondence Analysis

Ardelean, A.-T., & Weyrich, T. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00117 · Cited by 11 · see citing works

AnomalySD: One-for-all few-shot anomaly detection via pre-trained diffusion models

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Few-shot anomaly detection with adaptive feature transformation and descriptor construction

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A Survey on Anomaly Detection with Few-Shot Learning

Chen, J., Wang, C., Hong, Y., Mi, R., Zhang, L., Wu, Y., Wang, H., & Zhou, Y. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-77954-1_3 · Cited by 9 · see citing works

IAD-CLIP: Vision-Language Models for Zero-Shot Industrial Anomaly Detection

Li, Z., Ge, Y., Li, Q., & Meng, L. (2024) · International Conference on Advanced Mechatronic Systems · doi:10.1109/icamechs63130.2024.10818831 · Cited by 9 · see citing works

Unsupervised fur anomaly detection with B-spline noise-guided Multi-directional Feature Aggregation

Wang, X., Fan, J., Yan, F., Hu, H., Zeng, Z., & Huang, H. (2025) · The Visual Computer · doi:10.1007/s00371-024-03778-5 · Cited by 9 · see citing works

NFCF: Industrial Surface Anomaly Detection with Normalizing Flow Cross-Fitting Network

Yang, W., Song, K., Wang, Y., Wei, X., Tong, L., Chen, S., & Yan, Y. (2023) · Optics and Lasers in Engineering · doi:10.1016/j.optlaseng.2023.107655 · Cited by 9 · see citing works

MedicalCLIP: Anomaly-Detection Domain Generalization with Asymmetric Constraints

Hua, L., Luo, Y., Qi, Q., & Long, J. (2024) · Biomolecules · doi:10.3390/biom14050590 · Cited by 9 · see citing works

DC-AD: A Divide-and-Conquer Method for Few-Shot Anomaly Detection

Zhang, J., Yang, Z., & Song, Y. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.111360 · Cited by 8 · see citing works

Salvaging the Overlooked: Leveraging Class-Aware Contrastive Learning for Multi-Class Anomaly Detection

Fan, L., Huang, J., Di, D., Su, A., Song, T., Pagnucco, M., & Song, Y. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.01989 · Cited by 8 · see citing works

Patch Matching for Few-Shot Industrial Defect Detection

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ADFormer: Generalizable Few-Shot Anomaly Detection With Dual CNN-Transformer Architecture

Zhu, B., Gu, Z., Zhu, G., Chen, Y., Tang, M., & Wang, J. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3522624 · Cited by 8 · see citing works

FocusCLIP: Focusing on Anomaly Regions by Visual-Text Discrepancies

Zhao, Y., Sun, J., Zhang, L., & Lu, H. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3524784 · Cited by 7 · see citing works

Few-Shot Anomaly Detection via Personalization

Kwak, S., Jeong, J., Lee, H., Kim, W., Seo, D., Yun, W., Lee, W. J., & Shin, J. (2024) · IEEE Access · doi:10.1109/access.2024.3355021 · Cited by 7 · see citing works

A conditioned feature reconstruction network for few-shot classification

Song, B., Zhu, H., & Bi, Y. (2024) · Applied Intelligence · doi:10.1007/s10489-024-05516-9 · Cited by 7 · see citing works

FOCT: Few-shot Industrial Anomaly Detection with Foreground-aware Online Conditional Transport

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MadCLIP: Few-Shot Medical Anomaly Detection with CLIP

Shiri, M., Beyan, C., & Murino, V. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04978-0_40 · Cited by 6 · see citing works

One-shot unsupervised industrial anomaly detection: Enhanced performance under extreme data scarcity

Zhou, J., Wong, W. K., & Liao, F. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.112759 · Cited by 6 · see citing works

LR-IAD: Mask-Free Industrial Anomaly Detection with Logical Reasoning

Zeng, P., Pang, F., Wang, Z., & Yang, A. (2025) · Proceedings · doi:10.1109/icdm65498.2025.00097 · Cited by 6 · see citing works

FE-CLIP: Frequency Enhanced CLIP Model for Zero-Shot Anomaly Detection and Segmentation

Gong, T., Chu, Q., Liu, B., Zhou, W., & Yu, N. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.01971 · Cited by 6 · see citing works

RareCLIP: Rarity-Aware Online Zero-Shot Industrial Anomaly Detection

He, J., Cao, M., Peng, S., & Xie, Q. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.02269 · Cited by 6 · see citing works

FIND: Few-Shot Anomaly Inspection with Normal-Only Multi-Modal Data

Li, Y., Liu, F., Liao, J., Tian, S., Foo, C.-S., & Yang, X. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.02162 · Cited by 6 · see citing works

Detecting anomalies from liquid transfer videos in automated laboratory setting

Sarker, N. H., Hakim, Z. I. A., Dabouei, A., Uddin, M. R., Freyberg, Z., MacWilliams, A., Kangas, J. D., & Xu, M. (2023) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2023.1147514 · Cited by 6 · see citing works

Reconsidering learnable fine-grained text prompts for few-shot anomaly detection in visual-language models

Han, D., Xu, L., Zhou, M., Wan, J., Li, M., & Li, G. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106906 · Cited by 6 · see citing works

Adversarial neighbor perception network with feature distillation for anomaly detection

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DAS3D: Dual-Modality Anomaly Synthesis for 3D Anomaly Detection

Li, K., Dai, B., Fu, J., & Hou, X. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-92805-5_10 · Cited by 6 · see citing works

Learned Optical Flow-Based Defect Detection With Coarse-to-Fine Nonrigid Image Alignment

Deng, L., Meng, X., Guo, X., Chen, Y., Shan, H., Wu, S., Lin, J., Dong, M., & Zhu, L. (2025) · IEEE Sensors Journal · doi:10.1109/jsen.2025.3530104 · Cited by 5 · see citing works

FastADnet: Fast Anomaly Detection via Core-Feature Centered Cluster Reconstruction Network

Na, D., & Yoo, Y. (2025) · IEEE Access · doi:10.1109/access.2025.3540435 · Cited by 5 · see citing works

ZSCIAD: a preliminary exploration of zero-shot class incremental anomaly detection method

Guo, Y., Gao, X., & Yin, J. (2025) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-025-02682-6 · Cited by 5 · see citing works

VTFusion: A Vision–Text Multimodal Fusion Network for Few-Shot Anomaly Detection

Jiang, Y., Cao, Y., Cheng, Y., Zhang, Y., & Shen, W. (2026) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2026.3651630 · Cited by 5 · see citing works

ObjectCore – Efficient Few-shot Logical Anomaly Detection using Object Representations

Fučka, M., Zavrtanik, V., & Skočaj, D. (2026) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61042.2026.00376 · Cited by 5 · see citing works

Accelerating surface defect detection using normal data with an attention-guided feature distillation reconstruction network

Wang, H., He, X., Zhang, C., Liang, X., Zhu, P., Wang, X., Gui, W., Li, X., & Qian, X. (2025) · Measurement · doi:10.1016/j.measurement.2025.116702 · Cited by 5 · see citing works

AdapSyn: Anomaly detection based on triplet training with adaptive anomaly synthesis

Zhou, S., Lin, C., Chen, Z., Guo, B., & Zhao, Y. (2024) · Displays · doi:10.1016/j.displa.2024.102885 · Cited by 5 · see citing works

Unsupervised Brain MRI Anomaly Detection via Inter-Realization Channels

Madni, H. A., Shujat, H., Nardin, A. D., Zottin, S., & Foresti, G. L. (2025) · International Journal of Neural Systems · doi:10.1142/s0129065725500479 · Cited by 5 · see citing works

DFM: Differentiable Feature Matching for Anomaly Detection

Wu, S., Wang, Y., Liu, X., Yang, Y., Wang, R., Guo, G., Doermann, D., & Zhang, B. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.01418 · Cited by 4 · see citing works

Zero-shot printed circuit board defect detection via optical flow and reconstruction guidance

Yin, X., Wang, S., Wang, Y., Wang, P., Liu, Y., Shen, T., & Qiao, H. (2025) · Computers in Industry · doi:10.1016/j.compind.2025.104355 · Cited by 4 · see citing works

Three-Dimensional Defect Characterization of Ultrasonic Detection Based on GCNet Improved Contrast Learning Optimization

Wang, X., Wang, Q., Zhang, L., Yu, J., & Liu, Q. (2023) · Electronics · doi:10.3390/electronics12183944 · Cited by 4 · see citing works

A Novel Anomaly Detection Method for Strip Steel Based on Multi-Scale Knowledge Distillation and Feature Information Banks Network

Wen, X., Zhao, W., Yu, Z., Zhao, J., & Song, K. (2023) · Coatings · doi:10.3390/coatings13071171 · Cited by 3 · see citing works

A feature bank self-constructed strategy for zero-shot surface texture anomaly detection

Shen, H., Zio, E., Xiong, J., & Ma, Y. (2025) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2025.112145 · Cited by 3 · see citing works

Few-Shot Anomaly Detection via Category-Agnostic Registration Learning

Huang, C., Guan, H., Jiang, A., Wang, Y.-F., Spratling, M., Wang, X., & Zhang, Y. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3465446 · Cited by 20 · see citing works

Few-shot anomaly detection via adaptive graph feature registration

Wang, G., Xu, S., Li, K., & Ren, K. (2026) · Knowledge and Information Systems · doi:10.1007/s10115-025-02678-x

Registration and Convolutional Multidimensional Attention Module for Few-Shot Anomaly Detection

Xie, X., Xiong, S., Zheng, W., & Cai, T. (2025) · The European Journal on Artificial Intelligence · doi:10.1177/30504554241297616

Registration generation amalgamation network for few-shot anomaly detection

Gao, H., Dong, Y., Zheng, R., Zheng, L., & Li, Y. (2026) · Neurocomputing · doi:10.1016/j.neucom.2026.133347 · Cited by 1 · see citing works

Prototypical Learning Guided Context-Aware Segmentation Network for Few-Shot Anomaly DetectionPreprint

Jiang, Y., Cao, Y., & Shen, W. (2025) · arXiv (Cornell University) · doi:10.48550/arxiv.2512.15319