Registration Based Few-Shot Anomaly Detection

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Exploring Simple Siamese Representation Learning

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Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery

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SIGNATURE VERIFICATION USING A “SIAMESE” TIME DELAY NEURAL NETWORK

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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

He, H., Zhang, J., Chen, H., Chen, X., Li, Z., Chen, X., Wang, Y., Wang, C., & Xie, L. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i8.28690 · Cited by 271

AnomalyDiffusion: Few-Shot Anomaly Image Generation with Diffusion Model

Hu, T., Zhang, J., Yi, R., Du, Y., Chen, X., Liu, L., Wang, Y., & Wang, C. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i8.28696 · Cited by 192

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

Cao, Y., Zhang, J., Frittoli, L., Cheng, Y., Shen, W., & Boracchi, G. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72761-0_4 · Cited by 193

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

Wang, C., Zhu, W., Gao, B.-B., Gan, Z., Zhang, J., Gu, Z., Qian, S., Chen, M., & Ma, L. (2024) · 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/cvpr52733.2024.02159 · Cited by 190

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

Li, X., Zhang, Z., Tan, X., Chen, C., Qu, Y., Xie, Y., & Ma, L. (2024) · 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/cvpr52733.2024.01594 · Cited by 165

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

Guo, J., Lu, S., Zhang, W., Chen, F., Li, H., & Liao, H. (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/cvpr52734.2025.01900 · Cited by 170

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

Huang, C., Jiang, A., Feng, J., Zhang, Y., Wang, X., & Wang, Y. (2024) · 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/cvpr52733.2024.01081 · Cited by 143

Diversity-Measurable Anomaly Detection

Liu, W., Chang, H., Ma, B., Shan, S., & Chen, X. (2023) · 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/cvpr52729.2023.01169 · Cited by 133

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

Fang, Z., Wang, X., Li, H., Liu, J., Hu, Q., & Xiao, J. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01603 · Cited by 123

IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturing

Xie, G., Wang, J., Liu, J., Lyu, J., Liu, Y., Wang, C., Zheng, F., & Jin, Y. (2024) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2024.3357213 · Cited by 140

OmniAL: A Unified CNN Framework for Unsupervised Anomaly Localization

Zhao, Y. (2023) · 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/cvpr52729.2023.00382 · Cited by 104

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

Chen, X., Zhang, J., Tian, G., He, H., Zhang, W., Wang, Y., Wang, C., & Liu, Y. (2024) · Communications in computer and information science · doi:10.1007/978-981-97-9003-6_2 · Cited by 121

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

Zhu, J., & Pang, G. (2024) · 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/cvpr52733.2024.01688 · Cited by 101

Zero-shot versus Many-shot: Unsupervised Texture Anomaly Detection

Aota, T., Tong, L. T. T., & Okatani, T. (2023) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv56688.2023.00552 · Cited by 84

Exploring Intrinsic Normal Prototypes within a Single Image for Universal Anomaly Detection

Luo, W., Cao, Y., Yao, H., Zhang, X., Lou, J., Cheng, Y., Shen, W., & Yu, W. (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/cvpr52734.2025.00932 · Cited by 65

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

Schwartz, E., Arbelle, A., Karlinsky, L., Harary, S., Scheidegger, F., Doveh, S., & Giryes, R. (2024) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2024.103958 · Cited by 76

Dual-distribution discrepancy with self-supervised refinement for anomaly detection in medical images

Cai, Y., Chen, H., Yang, X. Y., Zhou, Y. C., & Cheng, K.-T. T. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102794 · Cited by 65

MedIAnomaly: A comparative study of anomaly detection in medical images

Cai, Y., Zhang, W., Chen, H., & Cheng, K.-T. T. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103500 · Cited by 79

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

Cao, Y., Xu, X., Cheng, Y., Sun, C., Du, Z., Gao, L., & Shen, W. (2025) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2025.3536165 · Cited by 63

Template-guided Hierarchical Feature Restoration for Anomaly Detection

Guo, H., Ren, L., Fu, J., Wang, Y., Zhang, Z., Lan, C., Wang, H., & Hou, X. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00593 · Cited by 59

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

Luo, W., Yao, H., Yu, W., & Li, Z. (2024) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2024.3368142 · Cited by 58

PromptAD: Zero-shot Anomaly Detection using Text Prompts

Li, Y., David, A. G., Liu, F., & Foo, C.-S. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00113 · Cited by 47

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

Zhang, X., Xu, M., Qiu, D., Yan, R., Lang, N., & Zhou, X. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72120-5_43 · Cited by 56

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

Li, H., Hu, J., Li, B., Chen, H., Zheng, Y., & Shen, C. (2024) · IEEE Transactions on Image Processing · doi:10.1109/tip.2024.3448263 · Cited by 49

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

Belton, N., Hagos, M. T., Lawlor, A., & Curran, K. M. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00299 · Cited by 43

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

Lin, Y., Chang, Y., Tong, X., Yu, J., Liotta, A., Huang, G., Song, W., Zeng, D., Wu, Z., Wang, Y., et al. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103139 · Cited by 61

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

Yao, X., Zhang, C., Li, R., Sun, J., & Liu, Z. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i4.25604 · Cited by 35

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

Liao, J., Xu, X., Nguyen, M. C., Goodge, A., & Foo, C.-S. (2024) · IEEE Transactions on Image Processing · doi:10.1109/tip.2024.3374048 · Cited by 33

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

Zhang, J., Ding, R., Ban, M., & Dai, L. (2024) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2024.3352358 · Cited by 34

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

Jiang, Z., Zhang, Y., Wang, Y., Li, J., & Gao, X. (2024) · Sensors · doi:10.3390/s24051368 · Cited by 31

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

Zhang, Z., Niu, C., Zhao, Z., Zhang, X., & Chen, X. (2025) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2025.3538070 · Cited by 29

Low-Shot Unsupervised Visual Anomaly Detection via Sparse Feature Representation

Zhang, F., Zhu, H., Cen, Y., Kan, S., Zhang, L., Vadakkepat, P., & Lee, T. H. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3420818 · Cited by 24

Unsupervised anomaly detection and localization with one model for all category

Tan, P., & Wong, W. K. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111533 · Cited by 22

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

Jiang, Y., Cao, Y., & Shen, W. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3463495 · Cited by 21

Foundation models and Transformers for anomaly detection: A survey

Ammar, M. B., Mendoza, A., Belkhir, N., Antoine Manzanera, B., & Franchi, G. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103517 · Cited by 25

Few-Shot Anomaly Detection via Category-Agnostic Registration Learning

Huang, C., Guan, H., Jiang, A., Zhang, Y., Spratling, M., Wang, X., & Wang, Y. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3465446 · Cited by 21

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

Fang, Q., Lv, W., & Su, Q. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3755635 · Cited by 18

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

Zhang, L., Zhang, S., Xie, G., Liu, J., Yan, H., Wang, J., Zheng, F., & Jin, Y. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00457 · Cited by 19

Few-shot online anomaly detection and segmentation

Wei, S., Wei, X., Ma, Z., Dong, S., Zhang, S., & Gong, Y. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112168 · Cited by 18

Produce Once, Utilize Twice for Anomaly Detection

Wang, S., Li, Q., Luo, H., Lv, C., & Zhang, Z. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3420775 · Cited by 18

Adversarial diffusion for few-shot scene adaptive video anomaly detection

Zahid, Y., Zarges, C., Tiddeman, B., & Han, J. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128796 · Cited by 19

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

Li, Y., Qian, G., Jiang, X., Jiang, Z., Wen, W., Zhang, S., Li, K., & Lao, Q. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3310716 · Cited by 18

Dual-path Frequency Discriminators for few-shot anomaly detection

Bai, Y., Zhang, J., Chen, Z., Dong, Y., Cao, Y., & Tian, G. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112397 · Cited by 20

Long-Tailed Anomaly Detection with Learnable Class Names

Ho, C.-H., Peng, K., & Vasconcelos, N. M. (2024) · 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/cvpr52733.2024.01182 · Cited by 21

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

Zhang, Q., Tian, K., Zhang, F., Li, J., Yang, K., Luo, L., Gao, X., & Peng, J. (2025) · NDT & E International · doi:10.1016/j.ndteint.2025.103388 · Cited by 17

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

Meng, S., Meng, W., Zhou, Q., Li, S., Hou, W., & He, S. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73013-9_20 · Cited by 19

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

Yu, M., Chen, Z., Liu, Y., Dong, C., & Song, K. (2025) · Measurement · doi:10.1016/j.measurement.2025.118377 · Cited by 22

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

Yang, H., Zhu, Z., Lin, C., Hui, W., Wang, S., & Zhao, Y. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3273681 · Cited by 15

Deep feature clustering for multi-class industrial image anomaly detection

Wang, R., Li, Z., Zheng, L., Wang, W., & Li, S. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113134 · Cited by 15

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

Jiang, J., He, Z., Wan, A., AL-Bukhaiti, K., Wang, K., Zhu, P., & Cheng, X. (2025) · Electronics · doi:10.3390/electronics14244785 · Cited by 14

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

Campos-Romero, M., Carranza-García, M., & Riquelme, J. C. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109088 · Cited by 12

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

Luo, A., Wen, G., Cheng, Y., Mei, S., Dong, H., & Liu, X. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128622 · Cited by 12

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

Zhang, J., Suganuma, M., & Okatani, T. (2024) · Machine Vision and Applications · doi:10.1007/s00138-024-01511-9 · Cited by 12

Few-shot anomaly detection using positive unlabeled learning with cycle consistency and co-occurrence features

An, S., Kim, J., Kim, S., Chikontwe, P., Jung, J., Jeon, H., & Park, S. H. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124890 · Cited by 11

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 14

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

Kohler, M., Mitsios, D., & Endisch, C. (2024) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2024.12.005 · Cited by 12

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

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

Yu, J., Gao, X., Wang, T., Lu, H., Li, B., Zhai, F., Xue, B., & Meng, Z. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110894 · Cited by 11

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

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

Yan, Z., Fang, Q., Lv, W., & Su, Q. (2026) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2026.104668 · Cited by 15

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

Zhengnan, H. U., Xiangrui, Z. E. N. G., Li, Y., Yin, Z., Meng, E., Zhu, L., & Kong, X. (2024) · Chinese Journal of Aeronautics · doi:10.1016/j.cja.2024.06.007 · Cited by 9

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

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

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

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

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

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

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 14

Patch Matching for Few-Shot Industrial Defect Detection

Chen, R., Yu, G., Qin, Z., Song, K., Tu, J., Jiang, X., Liang, D., & Peng, C. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3413170 · Cited by 8

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

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 8

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

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

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

Tian, L., Zhao, H., Lu, R., Wang, R., Wu, Y., Wang, L., He, X., & Liu, X. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3680771 · Cited by 10

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

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

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

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 12

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 12

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

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

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 8

Adversarial neighbor perception network with feature distillation for anomaly detection

Su, Y., Su, E., Wang, W., Jing, P., Ma, D., & Wang, F. L. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.126911 · Cited by 6

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 9

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

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