Learning Unsupervised Metaformer for Anomaly Detection

Deep Residual Learning for Image Recognition

He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works

Attention Is All You Need

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404 · see citing works

Image quality assessment: from error visibility to structural similarity

Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2003.819861 · Cited by 57,892 · see citing works

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

Finn, C., Abbeel, P., & Levine, S. (2017) · arXiv · doi:10.48550/arxiv.1703.03400 · Cited by 8,512 · see citing works

Overcoming catastrophic forgetting in neural networks

Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A. A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1611835114 · Cited by 7,899 · see citing works

Variational Inference with Normalizing FlowsPreprint

Rezende, D. J., & Mohamed, S. (2015) · International Conference on Machine Learning · doi:10.48550/arxiv.1505.05770 · Cited by 5,748 · see citing works

Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery

Schlegl, T., Seeböck, P., Waldstein, S. M., Schmidt-Erfurth, U., & Langs, G. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-59050-9_12 · Cited by 2,627 · see citing works

Global Contrast Based Salient Region Detection

Cheng, M., Mitra, N. J., Huang, X., Torr, P. H. S., & Hu, S. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2345401 · Cited by 2,463 · see citing works

MVTec AD — A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection

Bergmann, P., Fauser, M., Sattlegger, D., & Steger, C. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00982 · Cited by 2,203 · see citing works

Memorizing Normality to Detect Anomaly: Memory-Augmented Deep Autoencoder for Unsupervised Anomaly Detection

Gong, D., Liu, L., Le, V., Saha, B., Mansour, M. R., Venkatesh, S., & Van Den Hengel, A. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00179 · Cited by 1,930 · see citing works

Deep One-Class Classification

Ruff, L., Vandermeulen, R. A., Görnitz, N., Deecke, L., Siddiqui, S. A., Binder, A., Müller, E., & Kloft, M. (2018) · International Conference on Machine Learning · Cited by 1,719 · see citing works

Meshed-Memory Transformer for Image Captioning

Cornia, M., Stefanini, M., Baraldi, L., & Cucchiara, R. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01059 · Cited by 1,259 · see citing works

Learning Texture Transformer Network for Image Super-Resolution

Yang, F., Yang, H., Fu, J., Lu, H., & Guo, B. (2020) · 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/cvpr42600.2020.00583 · Cited by 1,034 · see citing works

Uninformed Students: Student-Teacher Anomaly Detection With Discriminative Latent Embeddings

Bergmann, P., Fauser, M., Sattlegger, D., & Steger, C. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00424 · Cited by 990 · see citing works

Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders

Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., & Steger, C. T. (2019) · International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) · doi:10.5220/0007364503720380 · Cited by 819 · see citing works

Reconstruction by inpainting for visual anomaly detection

Zavrtanik, V., Kristan, M., & Skočaj, D. (2020) · Pattern Recognition · doi:10.1016/j.patcog.2020.107706 · Cited by 752 · see citing works

Saliency Detection via Dense and Sparse Reconstruction

Li, X., Lu, H., Zhang, L., Ruan, X., & Yang, M. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.370 · Cited by 714 · see citing works

Pyramid Feature Attention Network for Saliency Detection

Zhao, T., & Wu, X. (2019) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2019.00320 · Cited by 700 · see citing works

GANomaly: Semi-supervised Anomaly Detection via Adversarial Training

Akçay, S., Atapour–Abarghouei, A., & Breckon, T. P. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-20893-6_39 · Cited by 613 · see citing works

Saliency Detection via Absorbing Markov Chain

Jiang, B., Zhang, L., Lu, H., Yang, C. F., & Yang, M. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.209 · Cited by 611 · see citing works

OCGAN: One-Class Novelty Detection Using GANs With Constrained Latent Representations

Perera, P., Nallapati, R., & Xiang, B. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00301 · Cited by 608 · see citing works

Latent Space Autoregression for Novelty Detection

Abati, D., Porrello, A., Calderara, S., & Cucchiara, R. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00057 · Cited by 508 · see citing works

Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flows

Rudolph, M., Wandt, B., & Rosenhahn, B. (2021) · 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv48630.2021.00195 · Cited by 489 · see citing works

Deep Anomaly Detection Using Geometric TransformationsPreprint

Golan, I., & El‐Yaniv, R. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.10917 · Cited by 479 · see citing works

Surface defect saliency of magnetic tile

Huang, Y., Qiu, C., & Yuan, K. (2018) · The Visual Computer · doi:10.1007/s00371-018-1588-5 · Cited by 460 · see citing works

Deep Structured Energy Based Models for Anomaly Detection

Zhai, S., Cheng, Y., Lu, W., & Zhang, Z. (2016) · arXiv · doi:10.48550/arxiv.1605.07717 · Cited by 334 · see citing works

Generative Probabilistic Novelty Detection with Adversarial Autoencoders

Pidhorskyi, S., Almohsen, R., Adjeroh, D. A., & Doretto, G. (2018) · arXiv · doi:10.48550/arxiv.1807.02588 · Cited by 255 · see citing works

Attention Guided Anomaly Localization in Images

Venkataramanan, S., Peng, K.-C., Singh, R. V., & Mahalanobis, A. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58520-4_29 · Cited by 247 · see citing works

Online Meta-LearningPreprint

Finn, C., Rajeswaran, A., Kakade, S. M., & Levine, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.08438 · Cited by 221 · see citing works

Deep Unsupervised Saliency Detection: A Multiple Noisy Labeling Perspective

Zhang, J., Zhang, T., Daf, Y., Harandi, M. T., & Hartley, R. I. (2018) · 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/cvpr.2018.00941 · Cited by 204 · see citing works

PolyTransform: Deep Polygon Transformer for Instance Segmentation

Liang, J., Homayounfar, N., Ma, W.-C., Xiong, Y., Hu, R., & Urtasun, R. (2020) · 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/cvpr42600.2020.00915 · Cited by 188 · see citing works

Automatic detection and classification of the ceramic tiles’ surface defects

Hanzaei, S. H., Afshar, A., & Barazandeh, F. (2016) · Pattern Recognition · doi:10.1016/j.patcog.2016.11.021 · Cited by 149 · see citing works

Encoding Structure-Texture Relation with P-Net for Anomaly Detection in Retinal Images

Zhou, K., Xiao, Y., Yang, J., Cheng, J., Liu, W., Luo, W., Gu, Z., Liu, J., & Gao, S. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58565-5_22 · Cited by 146 · see citing works

Sketchformer: Transformer-Based Representation for Sketched Structure

Ribeiro, L. S. F., Bui, T., Collomosse, J. P., & Ponti, M. A. (2020) · 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/cvpr42600.2020.01416 · Cited by 115 · see citing works

Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detection

Akcay, S., Atapour-Abarghouei, A., & Breckon, T. P. (2019) · 2019 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2019.8851808 · Cited by 103 · see citing works

Unsupervised Meta-Learning of Figure-Ground Segmentation via Imitating Visual Effects

Chen, D.-J., Chien, J.-T., Chen, H.-T., & Liu, T.-L. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33018159 · Cited by 3 · see citing works

Gradient-based Hyperparameter Optimization through Reversible Learning

Maclaurin, D., Duvenaud, D., & Adams, R. P. (2015) · arXiv · doi:10.48550/arxiv.1502.03492

Anomaly Detection via Reverse Distillation from One-Class Embedding

Deng, H., & Li, X. (2022) · 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/cvpr52688.2022.00951 · Cited by 959 · see citing works

Deep Industrial Image Anomaly Detection: A Survey

Liu, J., Xie, G., Wang, J., Li, S., Wang, C., Zheng, F., & Jin, Y. (2024) · Machine Intelligence Research · doi:10.1007/s11633-023-1459-z · Cited by 463 · see citing works

Registration Based Few-Shot Anomaly Detection

Huang, C., Guan, H., Jiang, A., Zhang, Y., Spratling, M., & Wang, Y.-F. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-20053-3_18 · Cited by 216 · see citing works

Self-supervised Sparse Representation for Video Anomaly Detection

Wu, J.-C., Hsieh, H., Chen, D.-J., Fuh, C., & Liu, T.-L. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19778-9_42 · Cited by 145 · see citing works

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 128 · see citing works

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 118 · see citing works

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 89 · see citing works

Nearest Neighbor Guidance for Out-of-Distribution Detection

Park, J., Jung, Y. G., & Teoh, A. B. J. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00162 · Cited by 46 · see citing works

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 39 · see citing works

Enhancing smart road safety with federated learning for Near Crash Detection to advance the development of the Internet of Vehicles

Djenouri, Y., Belbachir, A. N., Michalak, T., Belhadi, A., & Srivastava, G. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.108350 · Cited by 40 · see citing works

Focus the Discrepancy: Intra- and Inter-Correlation Learning for Image Anomaly Detection

Yao, X., Li, R., Qian, Z., Luo, Y., & Zhang, C. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00626 · Cited by 36 · see citing works

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 · see citing works

Two-stage reverse knowledge distillation incorporated and Self-Supervised Masking strategy for industrial anomaly detection

Tong, G., Li, Q., & Song, Y. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110611 · Cited by 35 · see citing works

Pixel-Level Anomaly Detection via Uncertainty-aware Prototypical Transformer

Huang, C., Liu, C., Zhang, Z., Wu, Z.-H., Wen, J., Jiang, Q., & Xu, Y. (2022) · ACM Multimedia · doi:10.1145/3503161.3548082 · Cited by 39 · see citing works

Self-supervised Feature Adaptation for 3D Industrial Anomaly Detection

Tu, Y., Zhang, B., Liu, L., Li, Y., Zhang, J., Wang, Y., Wang, C., & Zhao, C. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72627-9_5 · Cited by 28 · see citing works

From data analysis to intelligent maintenance: a survey on visual defect detection in aero-engines

Wu, P., Li, H., Luo, X., Hu, L., Yang, R., & Zeng, N. (2025) · Measurement Science and Technology · doi:10.1088/1361-6501/add6c8 · Cited by 28 · see citing works

Anomaly detection using a model-agnostic meta-learning-based variational auto-encoder for facility management

Moon, J., Noh, Y., Jung, S., Lee, J., & Hwang, E. (2023) · Journal of Building Engineering · doi:10.1016/j.jobe.2023.106099 · Cited by 28 · see citing works

Enhanced Multi-Scale Features Mutual Mapping Fusion Based on Reverse Knowledge Distillation for Industrial Anomaly Detection and Localization

Tong, G., Li, Q., & Song, Y. (2024) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2024.3350539 · Cited by 29 · see citing works

Anomaly Detection using Score-based Perturbation Resilience

Shin, W., Lee, J., Lee, T.-H., Lee, S., & Yun, J. P. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.02136 · Cited by 26 · see citing works

Asymmetric Teacher–Student Feature Pyramid Matching for Industrial Anomaly Detection

Zhu, J., Yan, P., Jiang, J., Cui, Y., & Xu, X. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3338681 · Cited by 21 · see citing works

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 21 · see citing works

ADPS: Asymmetric Distillation Postsegmentation for Image Anomaly Detection

Xing, P., Tang, H., Tang, J., & Li, Z. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3390806 · Cited by 19 · 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

A Reconstruction-Based Feature Adaptation for Anomaly Detection with Self-Supervised Multi-Scale Aggregation

Zuo, Z., Wu, Z., Chen, B., & Zhong, X. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10446766 · Cited by 17 · see citing works

SSMSPC: self-supervised multivariate statistical in-process control in discrete manufacturing processes

Biegel, T., Helm, P., Jourdan, N., & Metternich, J. (2023) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-023-02156-7 · Cited by 17 · see citing works

LARA: A Light and Anti-overfitting Retraining Approach for Unsupervised Time Series Anomaly Detection

Chen, F., Qin, Z., Zhou, M., Zhang, Y., Deng, S., Fan, L., Pang, G., & Wen, Q. (2024) · International World Wide Web Conference (WWW) · doi:10.1145/3589334.3645472 · Cited by 16 · see citing works

Dual-student knowledge distillation for visual anomaly detection

Hao, J., Huang, K., Chen, C., & Mao, J. (2024) · Complex & Intelligent Systems · doi:10.1007/s40747-024-01412-4 · Cited by 13 · see citing works

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 · see citing works

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 · see citing works

MÆIDM: multi-scale anomaly embedding inpainting and discrimination for surface anomaly detection

Sheng, S., Jing, J., Jiao, X. T., Wang, Y., & Dong, Z. (2023) · Machine Vision and Applications · doi:10.1007/s00138-023-01425-y · Cited by 11 · see citing works

XDNet: A Few-Shot Meta-Learning Approach for Cross-Domain Visual Inspection

Lee, X. Y., Vidyaratne, L., Alam, M., Farahat, A., Ghosh, D., Diaz, T. G., & Gupta, C. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00460 · Cited by 11 · see citing works

Uncertainty-aware prototypical learning for anomaly detection in medical images

Huang, C., Shi, Y., Zhang, B., & Lyu, K. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106284 · Cited by 14 · see citing works

Self-Supervised Autoencoders for Visual Anomaly Detection

Bauer, A., Nakajima, S., & Müller, K. (2024) · Mathematics · doi:10.3390/math12243988 · Cited by 11 · see citing works

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 · see citing works

CroSA: Unsupervised domain adaptation abnormal behavior detection via cross-space alignment

Mu, H., Zuo, X., Su, J., Hu, Y., Han, S., Qiao, B., Yuan, C., & Dang, L. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.126368 · Cited by 9 · see citing works

Asynchronous Decentralized Federated Anomaly Detection for 6G Networks

Liu, Y., & Yang, K. (2025) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2025.3538020 · Cited by 9 · see citing works

Meta-Learning Approach for Adaptive Anomaly Detection from Multi-Scenario Video Surveillance

Singh, D. K., Pant, D. R., Gautam, G., & Shrestha, B. (2025) · Applied Sciences · doi:10.3390/app15126687 · Cited by 9 · see citing works

Patch-Wise Augmentation for Anomaly Detection and Localization

Fan, L., Ding, Y., Pagnucco, M., & Song, Y. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10446994 · Cited by 9 · see citing works

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 · see citing works

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

Benchmarking framework for anomaly localization: Towards real-world deployment of automated visual inspection

Gangopadhyay, T., Hong, S., Roy, S., Shah, Y., & Cheong, L. L. (2023) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2023.05.024 · 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

Learning Transferable Representations for Image Anomaly Localization Using Dense Pretraining

He, H., Erfani, S., Gong, M., & Ke, Q. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00115 · Cited by 7 · see citing works

Incremental self-supervised learning based on transformer for anomaly detection and localization

Jin, W., Guo, F., Wu, Q., & Zhu, L. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.111978 · Cited by 6 · see citing works

SK-RD4AD: Skip-Connected Reverse Distillation for Robust One-Class Anomaly Detection

Park, E., Kim, T., Kim, M., Lee, H., & Lee, G.-J. (2025) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw67362.2025.00379 · 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

DewaterGAN: A Physics-Guided Unsupervised Image Water Removal for UAV in Coastal Zone

Yao, F., Tang, F., Wang, X., Wang, Y., Wang, S., Zhong, G., & Zhang, J. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3481491 · 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

A recover-then-discriminate framework for robust anomaly detection

Xing, P., Zhang, D., Tang, J., & Li, Z. (2025) · Science China Information Sciences · doi:10.1007/s11432-024-4291-4 · Cited by 8 · see citing works

Transformer-based contrastive learning framework for image anomaly detection

Fan, W., Shangguan, W., & Chen, Y. (2023) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-023-01840-7 · Cited by 6 · see citing works

An Improved Reverse Distillation Model for Unsupervised Anomaly Detection

Duc, N. V., Bach, H. H., & Trang, L. H. (2023) · International Conference on Ubiquitous Information Management and Communication (IMCOM) · doi:10.1109/imcom56909.2023.10035610 · Cited by 5 · see citing works

Knowledge Guided Visual Transformers for Intelligent Transportation Systems

Belhadi, A., Djenouri, Y., Belbachir, A. N., Michalak, T., & Srivastava, G. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3520487 · Cited by 5 · see citing works

Autoencoder-Like Knowledge Distillation Network for Anomaly Detection

Xu, C., Wang, B. J., Ni, D., Gan, J., Wu, M., & Zhou, W. (2023) · IEEE Access · doi:10.1109/access.2023.3314199 · Cited by 4 · see citing works

Memory guided dual discrepancy network with domain adaptation for few-shot anomaly detection

Zhang, F., Gong, L., Lin, S., & Zhao, Y. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.131711 · Cited by 3 · see citing works

Sea-CLIP: Mining Semantic-Aware Representations for Few-Shot Anomaly Detection with CLIP

Guo, X., Chen, Z., Castillo, C. D., Wang, H., & Liu, X. (2026) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61042.2026.00360 · Cited by 3 · see citing works

Enhancing Transfer Learning Reliability via Block-Wise Fine-Tuning

Barakat, B., & Huang, Q. (2023) · International Conference on Machine Learning and Applications · doi:10.1109/icmla58977.2023.00064 · Cited by 3 · see citing works

RDMS: Reverse distillation with multiple students of different scales for anomaly detection

Chen, Z., Lyu, C., Zhang, L., Li, S., & Xia, B. (2024) · IET Image Processing · doi:10.1049/ipr2.13210 · Cited by 3 · see citing works

Uniformaly: Towards Task-Agnostic Unified Framework for Visual Anomaly DetectionPreprint

Lee, Y., Lim, H., Jang, S., & Yoon, H. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4586132 · Cited by 3 · see citing works

Cross‐class pest and disease vegetation detection based on small sample registration

Liu, J., Linfeng, W., Yunsheng, W., Mingming, A., Wenfei, J., & Xu, S. (2023) · IET Image Processing · doi:10.1049/ipr2.12779 · Cited by 2 · see citing works

Deep support vector data description based on correntropy for few-shot anomaly detection

Shang, Q., Yang, J., Fu, F., & Ma, J. (2025) · Digital Signal Processing · doi:10.1016/j.dsp.2025.105074 · Cited by 2 · see citing works

Accurate Industrial Anomaly Detection and Localization Using Weakly-Supervised Residual Transformers

Li, H., Wu, J., Liu, D., Wu, L. Y., Chen, H., & Shen, C. (2026) · IEEE Transactions on Image Processing · doi:10.1109/tip.2026.3659337 · Cited by 3 · see citing works

GLASNet: A Global–Local Anomaly Generator and Salient Channel Feature Selector-Based Network for Accurate Few-Shot Industrial Anomaly Detection

Luo, A., Wen, G., Chen, Z., & Liu, X. (2025) · IEEE Sensors Journal · doi:10.1109/jsen.2025.3578508 · Cited by 2 · see citing works

Dual-Path Frequency Discriminators for Few-Shot Anomaly DetectionPreprint

Bai, Y., Zhang, J., Dong, Y., Cao, Y., & Tian, G. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4862099 · Cited by 2 · see citing works

Dual-Teacher Network with SSIM Based Reverse Distillation for Anomaly Detection

Li, W., Huang, R., & Wang, Z. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-8493-6_19 · Cited by 1 · see citing works

Towards Zero-shot 3D Anomaly Localization

Wang, Y., Peng, K., & Fu, Y. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00148 · Cited by 2 · see citing works

Neurosymbolic Visual Transform Based on Logic Tensor Network for Defect Detection

Djenouri, Y., Belbachir, A. N., Belhadi, A., & Michalak, T. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-92805-5_2 · Cited by 1 · see citing works

PADiff: Reconstruction From Patch to Pixel With Normality-Guided Diffusion Model for Unsupervised Anomaly Localization

Zuo, Z., Dong, J., Wu, Y., Qu, Y., & Wu, Z. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2025.3572438 · Cited by 1 · see citing works

Prior-Guided Feature Sampling and Restoration for Few-Shot Industrial Anomaly Detection

Hu, X., & Yang, H. (2026) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2026.3670014 · Cited by 1 · see citing works

High-frequency semantic hierarchical dynamic feature fusion network for chip surface defect detection

Zhang, F., Pan, B., Wang, Y., Zhou, C., Zhao, Y., & Duan, J. (2026) · Measurement Science and Technology · doi:10.1088/1361-6501/ae527b · Cited by 1 · see citing works

Normal and Abnormal Pathology Knowledge-Augmented Vision-Language Model for Anomaly Detection in Pathology Images

Song, J., Wang, J., Nguyen, A. T., Byeon, K., Ahn, S., Lee, S. H., & Kwak, J. T. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.02049 · Cited by 1 · see citing works

Anomaly Detection Algorithm Based on Multi-scale Bidirectional Distillation

Bao, S., Wei, S., & Chen, Z. (2024) · Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC) · doi:10.1109/ipec61310.2024.00081 · Cited by 1 · see citing works

FGSNet: A Finer-Grained Siamese Network for Industrial Few-Shot Anomaly Detection

Han, D., Xu, L., Zhou, M., Li, G., & Li, M. (2024) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc54092.2024.10831932 · Cited by 1 · see citing works

A Hierarchical Flow for Few-shot Anomaly Detection via Global-local Aggregation Strategy

Wan, Y., Fan, T., & Yao, L. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10889988 · Cited by 1 · see citing works

Extreme Inception Deep Learning Model for Multi-Factor Car Dent Severity Assessment and Repair Cost Estimation

Jawale, J., Shah, K., & Balsara, D. D. (2025) · International Conference on Computing for Sustainable Global Development · doi:10.23919/indiacom66777.2025.11115532 · Cited by 1 · see citing works

INTELLIGENT TELEMETRY DATA PROCESSING, VISUAL INSPECTION ON VIDEO AND LASER SCANNING DATA

Fralenko, V. P., & Shishkin, O. G. (2022) · Современные наукоемкие технологии (Modern High Technologies) · doi:10.17513/snt.39018

Adapsyn: Anomaly Detection Based on Triplet Training with Adaptive Anomaly Synthesis (Chinamm2024)Preprint

Zhou, S., Lin, C., Chen, Z., Guo, B., & Zhao, Y. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4889011

Few-Shot Industrial Anomaly Detection via Proxy Task Learning on Normal Samples

He, S., Xu, R., Huang, W., Chen, Y., & Wang, L. (2024) · IEEE Smart World Congress (SWC) · doi:10.1109/swc62898.2024.00220

A Diffusion-Based Framework with Feature Retrieval for Multi-Class Industrial Anomaly Detection

Chen, J., Zhang, R., & Zhong, G. (2025) · International Conference on Information Science, Electrical and Automation Engineering (ISEAE) · doi:10.1109/iseae64934.2025.11041745

Efficient Industrial Anomaly Detection via Knowledge Distillation

Zhang, Y., Zhu, J., Gu, X., & Li, M. (2025) · International Conference on Computer Vision, Image and Deep Learning (CVIDL) · doi:10.1109/cvidl65390.2025.11085554

Dscd: Dual-Student Collaborative Distillation for Industrial Anomaly DetectionPreprint

Ding, C., Xu, J., & Jiang, S. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5370573

C3L: Class-Centric Contrastive Learning for Long-Tailed Learning

Huang, K.-Y., Zou, L., Chen, C.-L., Ta, Y.-T., Liao, I.-B., Li, Y., & Cheng, W.-H. (2025) · International Conference on Information Technology and Education Technology (ITET) · doi:10.1109/itet65804.2025.11100929

Overview of AIGC Models for Industry Defect Detection

Xia, J., Zhu, B., Zhang, H., Li, Q., Ren, W., Dong, B., & Song, K. (2025) · International Conference on Electrical Technology and Automatic Control (ICETAC) · doi:10.1109/icetac65964.2025.11144040

Shared Knowledge Base for Multi Deep Learning in Defect Detection

Djenouri, Y., Belhadi, A., Srivastava, G., Belbachir, A. N., & Cano, A. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11229407

Unsupervised Anomaly Detection on Custom Silk-Screen Printing Patterns with Generative Models

Braccaioli, L., Corridori, C., & Conci, N. (2025) · European Workshop on Visual Information Processing · doi:10.1109/euvip66349.2025.11238859

A Deep-Supervised Knowledge Distillation Method for Industrial Anomaly Detection

He, Y., Sun, L., Li, Y., Liu, G., & Zhang, J. (2025) · International Conference on Signal Processing Systems (ICSPS) · doi:10.1109/icsps66615.2025.11347646

Transferring Vision-Language Models for Zero-Shot Anomaly Detection via Adaptive Feature Fusion

Quan, C., Shi, C., Qin, L., & Wu, G. (2025) · International Symposium on Artificial Intelligence and Big Data (AIBDF) · doi:10.1109/aibdf67964.2025.11440857

Semi-supervised deep learning for robust anomaly detection in smart manufacturing systems

Le, T. (2024) · doi:10.70675/19d23228zf030z4df7za3abz596d36577818

Fine-Tuning CLIP Models with Convolutional Features for Medical Anomaly Detection

Fu, J., & Xue, M. (2025) · Asian Conference on Artificial Intelligence Technology (ACAIT) · doi:10.1109/acait67930.2025.11522646

Enhancing medical anomaly detection via text-adapted few-shot learning with visual-language models

Mao, K., Hou, S., Fang, H., Zhao, J., & Xiao, X. (2026) · The Visual Computer · doi:10.1007/s00371-026-04498-8

Deployment-Oriented Memory-Based Visual Inspection of Injection-Molded Parts

Raza, M. M., Ali, B. B., Qureshi, M. S., Rizvi, O., Anees, M., Khalid, A., Wasay, M. A., Hassan, W., & Ahsan, U. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-26217-2_16

Multi-scale feature-enhanced Auto-UNet with Cross Graph Mamba for high resolution image anomaly detection

Liu, L., & Wei, W. (2026) · Journal of King Saud University - Computer and Information Sciences · doi:10.1007/s44443-026-01286-1

Neighborhood Attention-based Feature Reconstruction for Image Anomaly Detection and Localization

Xian, W., Chen, Y., Chen, B., Leong Hou, U., Liu, S., Feng, Y., Zhou, M., & Kwong, S. (2026) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3786784

Removing Anomalies as Noises for Industrial Defect Localization

Lu, F., Yao, X., Fu, C., & Jia, J. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01481 · Cited by 63 · see citing works

Attention-Guided Perturbation Network for Industrial Anomaly DetectionPreprint

Huang, T., Kong, W., Liu, H., Chen, S., Zhou, B., Zhang, L., & He, X. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2408.07490

Image anomaly detection with a unified transformer model guided by dual-feature

Wang, Y., Jing, J., & Zhang, X. (2025) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2025.100892 · Cited by 3 · see citing works

Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt

Gao, B.-B. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72855-6_26 · Cited by 14 · see citing works