The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
PaDiM: A Patch Distribution Modeling Framework for Anomaly Detection and Localization
Open accessOverview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“PaDiM requires the batch-inverse of the multidimensional covariance tensor, which makes it challenging to scale up to larger CNNs due to the increased feature size.”
From Deep Industrial Image Anomaly Detection: A Survey (2024) · cites this paper as [73]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“PatchCore [68] , SPADE [63] and PaDim [73] conduct the ablation study on 16 normal training samples.”
From Deep Industrial Image Anomaly Detection: A Survey (2024) · cites this paper as [73]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Despite the rapid advance of unsupervised anomaly detection, existing methods require to train separate models for different objects.”
From A Unified Model for Multi-Class Anomaly Detection (2022) · cites this paper as [11]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Feature-based approaches [17,18,19] commonly employ networks pre-trained on ImageNet [20] to map original images into a more distinguishable feature space. These methods are typically characterized by low training costs and outstanding performance.”
From An Unsupervised Method for Industrial Image Anomaly Detection with Vision Transformer-Based Autoencoder (2024) · cites this paper as [19]
The cited source says what the claim attributes to it.
In the citing paper
“Empirically and similar to [10] and [14], we found aggregation of multiple feature hierarchies to offer some benefit.”
From Towards Total Recall in Industrial Anomaly Detection (2022) · cites this paper as [14]
The cited source says what the claim attributes to it.
In the citing paper
“Relying on the fact that an image can be already classified as anomalous as soon as a single patch is anomalous [14, 56], PatchCore achieves this by utilizing locally aggregated, mid-level features patches.”
From Towards Total Recall in Industrial Anomaly Detection (2022) · cites this paper as [14]
The cited source says what the claim attributes to it.
In the citing paper
“More global context can also be achieved by moving down the network hierarchy (see e.g. [10, 14]), however at the cost of reduced resolution and heavier ImageNet class bias (§3.1).”
From Towards Total Recall in Industrial Anomaly Detection (2022) · cites this paper as [14]
The cited source says what the claim attributes to it.
In the citing paper
“Consequently, the memory bank size is determined solely by the image resolution and not by the size of the training set.”
From Deep Industrial Image Anomaly Detection: A Survey (2024) · cites this paper as [73]
The cited source says what the claim attributes to it.
In the citing paper
“PaDim [73] employs multivariate Gaussian distributions to construct a probabilistic representation of the normal class.”
From Deep Industrial Image Anomaly Detection: A Survey (2024) · cites this paper as [73]
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“With coreset subsampling, Patchcore can be made even faster, with lower inference times than even PaDiM while retaining state-of-the-art image-level anomaly detection and segmentation performance.”
From Towards Total Recall in Industrial Anomaly Detection (2022) · cites this paper as [14]
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
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
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
Auto-Encoding Variational Bayes
Kingma, D. P., & Welling, M. (2013) · arXiv · doi:10.48550/arxiv.1312.6114 · Cited by 20,067
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Tan, M., & Le, Q. V. (2019) · arXiv · doi:10.48550/arxiv.1905.11946 · Cited by 17,072
Nearest neighbor pattern classification
Cover, T., & Hart, P. (1967) · IEEE Transactions on Information Theory · doi:10.1109/tit.1967.1053964 · Cited by 16,936
LIII. On lines and planes of closest fit to systems of points in space
Pearson, K. (1901) · The London Edinburgh and Dublin Philosophical Magazine and Journal of Science · doi:10.1080/14786440109462720 · Cited by 12,864
Zagoruyko, S., & Komodakis, N. (2016) · Procedings of the British Machine Vision Conference 2016 · doi:10.5244/c.30.87 · Cited by 7,244
On the generalized distance in statistics
Mahalanobis, P. C. (1936) · DOAJ (DOAJ: Directory of Open Access Journals) · Cited by 6,068
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
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
Research works printed but not linked: 1
- Akcay, S., Atapour-Abarghouei, A., Breckon, T.P.: GANomaly: semi-supervised anomaly detection via adversarial training. In: Jawahar, C.V., Li, H., Mori, G., Schindler, K. (eds.) ACCV 2018. LNCS, vol. 11363, pp. 622–637. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20893-6_39 · DOI
Cited by
Sorted by citations
Towards Total Recall in Industrial Anomaly Detection
Roth, K., Pemula, L., Zepeda, J., Scholkopf, B., Brox, T., & Gehler, P. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01392 · Cited by 1,810
“6% , and stride s = 3 an AUROC of 96 .”
BackgroundMethodResultInfluentialDRÆM – A discriminatively trained reconstruction embedding for surface anomaly detection
Zavrtanik, V., Kristan, M., & Skocaj, D. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00822 · Cited by 1,086
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 1,016
SimpleNet: A Simple Network for Image Anomaly Detection and Localization
Liu, Z., Zhou, Y., Xu, Y., & Wang, Z. (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/cvpr52729.2023.01954 · Cited by 748
SPot-the-Difference Self-supervised Pre-training for Anomaly Detection and Segmentation
Zou, Y., Jeong, J., Pemula, L., Zhang, D., & Dabeer, O. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-20056-4_23 · Cited by 651
Gudovskiy, D., Ishizaka, S., & Kozuka, K. (2022) · 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv51458.2022.00188 · Cited by 669
WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation
Jeong, J., Zou, Y., Kim, T., Zhang, D., Ravichandran, A., & Dabeer, O. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01878 · Cited by 585
Surface Defect Detection Methods for Industrial Products: A Review
Chen, Y., Ding, Y., Zhao, F., Zhang, E., Wu, Z., & Shao, L. (2021) · Applied Sciences · doi:10.3390/app11167657 · Cited by 457
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
“PatchCore [69], SPADE [64], PaDim [74] conduct the ablation study on 16 normal training samples.”
InfluentialEfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
Batzner, K., Heckler, L., & König, R. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00020 · Cited by 469
GAN-based anomaly detection: A review
Xia, X., Pan, X., Li, N., He, X., Ma, L., Zhang, X., & Ding, N. (2022) · Neurocomputing · doi:10.1016/j.neucom.2021.12.093 · Cited by 423
Fully Convolutional Cross-Scale-Flows for Image-based Defect Detection
Rudolph, M., Wehrbein, T., Rosenhahn, B., & Wandt, B. (2022) · 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv51458.2022.00189 · Cited by 348
CFA: Coupled-Hypersphere-Based Feature Adaptation for Target-Oriented Anomaly Localization
Lee, S., Lee, S., & Song, B. C. (2022) · IEEE Access · doi:10.1109/access.2022.3193699 · Cited by 370
Defect Detection Methods for Industrial Products Using Deep Learning Techniques: A Review
Saberironaghi, A., Ren, J., & El–Gindy, M. (2023) · Algorithms · doi:10.3390/a16020095 · Cited by 343
AnomalyGPT: Detecting Industrial Anomalies Using Large Vision-Language Models
Gu, Z., Zhu, B., Zhu, G., Chen, Y., Tang, M., & Wang, J. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i3.27963 · Cited by 400
DeSTSeg: Segmentation Guided Denoising Student-Teacher for Anomaly Detection
Zhang, X., Li, S., Li, X., Huang, P., Shan, J., & Chen, T. (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/cvpr52729.2023.00381 · Cited by 326
Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection
Ristea, N.-C., Madan, N., Ionescu, R. T., Nasrollahi, K., Khan, F. S., Moeslund, T. B., & Shah, M. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01321 · Cited by 315
Yang, M., Wu, P., & Feng, H. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.105835 · Cited by 257
Asymmetric Student-Teacher Networks for Industrial Anomaly Detection
Rudolph, M., Wehrbein, T., Rosenhahn, B., & Wandt, B. (2023) · 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv56688.2023.00262 · Cited by 280
Omni-Frequency Channel-Selection Representations for Unsupervised Anomaly Detection
Liang, Y., Zhang, J., Zhao, S., Wu, R., Liu, Y., & Pan, S. (2023) · IEEE Transactions on Image Processing · doi:10.1109/tip.2023.3293772 · Cited by 265
Multimodal Industrial Anomaly Detection via Hybrid Fusion
Wang, Y., Peng, J., Zhang, J., Yi, R., Wang, Y., & Wang, C. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.00776 · Cited by 246
Natural Synthetic Anomalies for Self-supervised Anomaly Detection and Localization
Schlüter, H. M., Tan, J., Hou, B., & Kainz, B. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19821-2_27 · Cited by 223
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
RealNet: A Feature Selection Network with Realistic Synthetic Anomaly for Anomaly Detection
Zhang, X., Xu, M., & Zhou, X. (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/cvpr52733.2024.01580 · Cited by 254
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
FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows
Yu, J., Zheng, Y., Wang, X., Li, W., Wu, Y., Zhao, R., & Wu, L. (2021) · arXiv · doi:10.48550/arxiv.2111.07677 · Cited by 210
Deep Learning for Unsupervised Anomaly Localization in Industrial Images: A Survey
Tao, X., Gong, X., Zhang, X., Yan, S., & Adak, C. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3196436 · Cited by 228
DSR – A Dual Subspace Re-Projection Network for Surface Anomaly Detection
Zavrtanik, V., Kristan, M., & Skočaj, D. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19821-2_31 · Cited by 194
Ježek, Š., Jonák, M., Bürget, R., Dvořák, P., & Skotak, M. (2021) · International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT) · doi:10.1109/icumt54235.2021.9631567 · Cited by 202
UTRAD: Anomaly detection and localization with U-Transformer
Chen, L., You, Z., Zhang, N., Xi, J., & Le, X. (2021) · Neural Networks · doi:10.1016/j.neunet.2021.12.008 · Cited by 177
Anomalib: A Deep Learning Library for Anomaly Detection
Akçay, S., Ameln, D., Vaidya, A., Lakshmanan, B., Ahuja, N., & Genc, U. (2022) · Proceedings - International Conference on Image Processing · doi:10.1109/icip46576.2022.9897283 · Cited by 173
A Survey on Unsupervised Anomaly Detection Algorithms for Industrial Images
Cui, Y., Liu, Z., & Lian, S. (2023) · IEEE Access · doi:10.1109/access.2023.3282993 · Cited by 185
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
PyramidFlow: High-Resolution Defect Contrastive Localization Using Pyramid Normalizing Flow
Lei, J., Hu, X., Wang, Y., & Liu, D. (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/cvpr52729.2023.01359 · Cited by 157
Collaborative Discrepancy Optimization for Reliable Image Anomaly Localization
Cao, Y., Xu, X., Liu, Z., & Shen, W. (2023) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2023.3241579 · Cited by 154
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
Unsupervised Surface Anomaly Detection with Diffusion Probabilistic Model
Zhang, X., Li, N., Li, J., Dai, T., Jiang, Y., & Xia, S. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00624 · Cited by 152
Zhang, Z., Zhao, Z., Zhang, X., Sun, C., & Chen, X. (2023) · Computers in Industry · doi:10.1016/j.compind.2023.103990 · Cited by 149
A survey of deep learning for industrial visual anomaly detection
Li, Z., Yan, Y., Wang, X., Ge, Y., & Meng, L. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11287-7 · Cited by 163
Informative knowledge distillation for image anomaly segmentation
Cao, Y., Wan, Q., Shen, W., & Gao, L. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.108846 · Cited by 130
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
Transformer architecture based on mutual attention for image-anomaly detection
Zhang, M., & Tian, X. (2023) · Virtual Reality & Intelligent Hardware · doi:10.1016/j.vrih.2022.07.006 · Cited by 126
Prototypical Residual Networks for Anomaly Detection and Localization
Zhang, H., Wu, Z., Wang, Z., Chen, Z., & Jiang, 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/cvpr52729.2023.01562 · Cited by 129
Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detection
Horwitz, E., & Hoshen, Y. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00298 · Cited by 151
ReConPatch : Contrastive Patch Representation Learning for Industrial Anomaly Detection
Hyun, J., Kim, S., Jeon, G., Kim, S. H., Bae, K., & Kang, B. J. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00205 · Cited by 140
Lee, Y., & Kang, P. (2022) · IEEE Access · doi:10.1109/access.2022.3171559 · Cited by 123
Zipfel, J., Verworner, F., Fischer, M., Wieland, U., Kraus, M., & Zschech, P. (2023) · Computers & Industrial Engineering · doi:10.1016/j.cie.2023.109045 · Cited by 134
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
MSFlow: Multiscale Flow-Based Framework for Unsupervised Anomaly Detection
Zhou, Y., Xu, X., Song, J., Shen, F., & Shen, H. T. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3344118 · Cited by 135
Deep One-Class Classification via Interpolated Gaussian Descriptor
Chen, Y., Tian, Y., Pang, G., & Carneiro, G. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i1.19915 · Cited by 123
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
Explicit Boundary Guided Semi-Push-Pull Contrastive Learning for Supervised Anomaly Detection
Yao, X., Li, R., Zhang, J., Sun, J., & Zhang, C. (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/cvpr52729.2023.02346 · Cited by 129
PNI: Industrial Anomaly Detection using Position and Neighborhood Information
Bae, J., Lee, J.-H., & Kim, S. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00586 · Cited by 138
Deep learning in automated power line inspection: A review
Faisal, M. A. A., Mecheter, I., Qiblawey, Y., Fernandez, J. H., Chowdhury, M. E. H., & Serkan Kiranyaz, B. (2025) · Applied Energy · doi:10.1016/j.apenergy.2025.125507 · Cited by 141
Unsupervised Anomaly Detection for Surface Defects With Dual-Siamese Network
Tao, X., Zhang, D., Ma, W., Hou, Z., Lu, Z., & Adak, C. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3142326 · Cited by 116
Self-Supervised Masked Convolutional Transformer Block for Anomaly Detection
Madan, N., Ristea, N.-C., Ionescu, R. T., Nasrollahi, K., Khan, F. S., Moeslund, T. B., & Shah, M. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3322604 · Cited by 131
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
DiffusionAD: Norm-Guided One-Step Denoising Diffusion for Anomaly Detection
Zhang, H., Wang, Z., Zeng, D., Wu, Z., & Jiang, Y. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3570494 · Cited by 194
A Unified Model for Multi-Class Anomaly Detection
You, Z., Cui, L., Shen, Y., Yang, K., Lu, X., Zheng, Y., & Le, X. (2022) · Advances in Neural Information Processing Systems 35 · doi:10.52202/068431-0330 · Cited by 303
Self-supervised anomaly detection in computer vision and beyond: A survey and outlook
Hojjati, H., Ho, T. K. K., & Armanfard, N. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106106 · Cited by 149
Anomaly Detection in 3D Point Clouds using Deep Geometric Descriptors
Bergmann, P., & Sattlegger, D. (2023) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv56688.2023.00264 · Cited by 102
Multimodal Industrial Anomaly Detection by Crossmodal Feature Mapping
Costanzino, A., Ramirez, P. Z., Lisanti, G., & Stefano, L. D. (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/cvpr52733.2024.01631 · Cited by 114
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
BMAD: Benchmarks for Medical Anomaly Detection
Bao, J., Sun, H., Deng, H., He, Y., Zhang, Z., & Li, X. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00408 · Cited by 122
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
Machine Learning-Enabled Prediction of 3D-Printed Microneedle Features
Sarabi, M. R., Alseed, M. M., Karagoz, A. A., & Taşoğlu, S. (2022) · Biosensors · doi:10.3390/bios12070491 · Cited by 90
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
Gaussian Anomaly Detection by Modeling the Distribution of Normal Data in Pretrained Deep Features
Rippel, O., Mertens, P., Konig, E., & Merhof, D. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3098381 · Cited by 88
AA-CLIP: Enhancing Zero-Shot Anomaly Detection via Anomaly-Aware CLIP
Ma, W., Zhang, X., Yao, Q., Tang, F., Wu, C., Li, Y., Yan, R. T., Jiang, Z., & Zhou, S. (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/cvpr52734.2025.00447 · Cited by 84
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
DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detectionPreprint
Zavrtanik, V., Kristan, M., & Skočaj, D. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.07610 · Cited by 81
Anomaly Detection with Conditioned Denoising Diffusion Models
Mousakhan, A., Brox, T., & Tayyub, J. (2023) · German Conference on Pattern Recognition · doi:10.1007/978-3-031-85181-0_12 · Cited by 139
Multi-Scale Patch-Based Representation Learning for Image Anomaly Detection and Segmentation
Tsai, C.-C., Wu, T.-H., & Lai, S. (2022) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv51458.2022.00312 · Cited by 83
Üzen, H., Türkoğlu, M., Yanikoglu, B. A., & Hanbay, D. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118269 · Cited by 79
Remembering Normality: Memory-guided Knowledge Distillation for Unsupervised Anomaly Detection
Gu, Z., Liu, L., Chen, X., Yi, R., Zhang, J., Wang, Y., Wang, C., Shu, A., Jiang, G., & Ma, L. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01503 · Cited by 83
Yao, H., Liu, M., Yin, Z., Yan, Z., Hong, X., & Zuo, W. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73209-6_1 · Cited by 90
SQUID: Deep Feature In-Painting for Unsupervised Anomaly Detection
Xiang, T., Zhang, Y., Lu, Y., Yuille, A., Zhang, C., Cai, W., & Zhou, Z. (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/cvpr52729.2023.02288 · Cited by 74
EasyNet: An Easy Network for 3D Industrial Anomaly Detection
Chen, R., Xie, G., Liu, J., Wang, J., Luo, Z., Wang, J., & Zheng, F. (2023) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3581783.3611876 · Cited by 91
The Eyecandies Dataset for Unsupervised Multimodal Anomaly Detection and Localization
Bonfiglioli, L., Toschi, M., Silvestri, D., Fioraio, N., & Gregorio, D. D. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-26348-4_27 · Cited by 76
AnomalyDINO: Boosting Patch-based Few-Shot Anomaly Detection with DINOv2
Damm, S., Laszkiewicz, M., Lederer, J., & Fischer, A. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00136 · Cited by 88
Zheng, Y., Wang, X., Deng, R., Bao, T., Zhao, R., & Wu, L. (2022) · 2022 IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme52920.2022.9859925 · Cited by 80
Biswas, A. A., Dhondale, M. R., Agrawal, A. K., Serrano, D. R., Mishra, B., & Kumar, D. K. (2024) · Drug Delivery and Translational Research · doi:10.1007/s13346-023-01510-9 · Cited by 68
Zhou, Q., He, S., Liu, H., Chen, T., & Chen, J. (2022) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2022.3218587 · Cited by 68
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
Visual Anomaly Detection via Partition Memory Bank Module and Error Estimation
Xing, P., & Li, Z. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3237562 · Cited by 67
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 68
FiLo: Zero-Shot Anomaly Detection by Fine-Grained Description and High-Quality Localization
Gu, Z., Zhu, B., Zhu, G., Chen, Y., Li, H., Tang, M., & Wang, J. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3680685 · Cited by 81
ADTR: Anomaly Detection Transformer with Feature Reconstruction
You, Z., Yang, K., Luo, W., Cui, L., Zheng, Y., & Le, X. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-30111-7_26 · Cited by 61
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-Attention Transformer and Discriminative Flow for Industrial Visual Anomaly Detection
Yao, H., Luo, W., Yu, W., Zhang, X., Qiang, Z., Luo, D., & Shi, H. (2023) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2023.3322156 · Cited by 69
Liu, R., Xiao, D., Lin, D., & Zhang, W. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3358871 · Cited by 58
Self-Attention Memory-Augmented Wavelet-CNN for Anomaly Detection
Wu, K., Zhu, L., Shi, W., Wang, W., & Wu, J. (2022) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2022.3211839 · Cited by 58
Learning Global-Local Correspondence With Semantic Bottleneck for Logical Anomaly Detection
Yao, H., Yu, W., Luo, W., Qiang, Z., Luo, D., & Zhang, X. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3314801 · Cited by 60
Li, W., Xu, X., Gu, Y., Zheng, B., Gaol, S., & Wu, 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/cvpr52733.2024.02096 · Cited by 57
Deep learning-based anomaly detection from ultrasonic images
Posilović, L., Medak, D., Milković, F., Subašić, M., Budimir, M., & Lončarić, S. (2022) · Ultrasonics · doi:10.1016/j.ultras.2022.106737 · Cited by 61
ITran: A novel transformer-based approach for industrial anomaly detection and localization
Cai, X., Xiao, R., Zeng, Z., Gong, P., & Ni, Y. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106677 · Cited by 62
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
Prior Normality Prompt Transformer for Multiclass Industrial Image Anomaly Detection
Yao, H., Cao, Y., Luo, W., Zhang, W., Yu, W., & Shen, W. (2024) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2024.3413322 · Cited by 60
Anomaly detection in electroluminescence images of heterojunction solar cells
Коровин, А. Н., Vasilev, ̵., Egorov, F., Saykin, D., Terukov, E. I., Shakhray, I., Евгеньевич, Ж. Л., & Budennyy, S. (2023) · Solar Energy · doi:10.1016/j.solener.2023.04.059 · Cited by 56
Jiang, Y., Cao, Y., & Shen, W. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110982 · Cited by 52
Metadata
- Authors
- Defard, T., Setkov, A., Loesch, A., & Audigier, R.
- Affiliations
- Commissariat à l'Énergie Atomique et aux Énergies Alternatives (FR)
- Corresponding author
- Setkov, Aleksandr
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2021
- Volume
- 12664
- Pages
- 475-489
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W3099658315
- Cited by
- 1,415
- References
- 32
- Citations by year
- 2026: 325 · 2025: 391 · 2024: 367 · 2023: 217 · 2022: 88 · 2021: 27
- FWCI
- 80.73
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- HAL (submittedVersion, other-oa)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
- Keywords
- anomaly detection, anomaly localization, one-class learning, multivariate normal distribution, patch embedding, pre-trained convolutional neural networks, MVTec AD
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
