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Registration Based Few-Shot Anomaly Detection
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“There are two settings in FSAD. The first setting is meta-learning [141] .”
From Deep Industrial Image Anomaly Detection: A Survey (2024) · cites this paper as [141]
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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 Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
Image registration methods: a survey
Zitová, B., & Flusser, J. (2003) · Image and Vision Computing · doi:10.1016/s0262-8856(03)00137-9 · Cited by 6,423
International Conference on Learning Representations (ICLR 2013)
松尾, 豊. (2013) · 人工知能学会誌 = Journal of Japanese Society for Artificial Intelligence · Cited by 6,286
Estimating the Support of a High-Dimensional Distribution
Schölkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., & Williamson, R. C. (2001) · Neural Computation · doi:10.1162/089976601750264965 · Cited by 6,168
Jaderberg, M., Simonyan, K., Zisserman, A., & Kavukcuoglu, K. (2015) · arXiv · doi:10.48550/arxiv.1506.02025 · Cited by 5,137
A survey of image registration techniques
Brown, L. G. (1992) · ACM Computing Surveys · doi:10.1145/146370.146374 · Cited by 4,079
Exploring Simple Siamese Representation Learning
Chen, X., & He, K. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01549 · Cited by 3,725
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
SIGNATURE VERIFICATION USING A “SIAMESE” TIME DELAY NEURAL NETWORK
BROMLEY, J., BENTZ, J. W., BOTTOU, L., GUYON, I., LECUN, Y., MOORE, C., SÄCKINGER, E., & SHAH, R. (1994) · Series in Machine Perception and Artificial Intelligence · doi:10.1142/9789812797926_0003 · Cited by 2,350
Research works printed but not linked: 12
- 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
- Cheng, M.M., Mitra, N.J., Huang, X., Torr, P.H., Hu, S.M.: Global contrast based salient region detection. IEEE Trans. Pattern Anal. Mach. Intell. 37(8), 569–582 (2014)
- Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks. In: International Conference on Machine Learning (ICML), pp. 1126–1135 (2017)
- Golan, I., El-Yaniv, R.: Deep anomaly detection using geometric transformations. Adv. Neural Inf. Process. Syst. (NeurIPS) 31 (2018)
- He, J., Hong, R., Liu, X., Xu, M., Wang, M.: Revisiting deep local descriptor for improved few-shot classification. In: 30th International Joint Conference on Artificial Intelligence (IJCAI), pp. 3420–3426 (2021)
- Huang, C., Ye, F., Zhao, P., Zhang, Y., Wang, Y., Tian, Q.: ESAD: end-to-end semi-supervised anomaly detection. In: The 32nd British Machine Vision Conference (BMVC) (2022)
- Van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(11), 2579–2605 (2008)
- Salehi, M., Sadjadi, N., Baselizadeh, S., Rohban, M.H., Rabiee, H.R.: Multiresolution knowledge distillation for anomaly detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 14902–14912 (2021) · DOI · Paper page
- Snell, J., Swersky, K., Zemel, R.: Prototypical networks for few-shot learning. Adv. Neural Inf. Process. Syst. (NeurIPS) 30 (2017)
- Sung, F., Yang, Y., Zhang, L., Xiang, T., Torr, P.H., Hospedales, T.M.: Learning to compare: relation network for few-shot learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1199–1208 (2018) · DOI
- Yang, S., Liu, L., Xu, M.: Free lunch for few-shot learning: distribution calibration. In: International Conference on Learning Representations (ICLR) (2021)
- Zong, B., et al.: Deep autoencoding gaussian mixture model for unsupervised anomaly detection. In: International Conference on Learning Representations (ICLR) (2018)
Cited by
Sorted by citations
Items 201–216 of 216
Vision-guided multi-image prompt learning for zero and few-shot pipeline anomaly detection
Wu, C., Wang, Y., Qiao, B., & Zhang, H. (2026) · Multimedia Systems · doi:10.1007/s00530-026-02652-9
Self-citationBhuvana, J., Mirnalinee, T. T., Mohan, H., & Olirva, M. (2026) · Multimedia Tools and Applications · doi:10.1007/s11042-026-21844-z
CoPartAD: Cross-Image Consistent Part-Aware Few-Shot Anomaly Detection
Wu, Y., Chen, Y., & Liang, E. (2026) · International Conference on Intelligent Systems and Robotics (CISR) · doi:10.1109/cisr70722.2026.11680272
TAMCA: Time-Aware Multi-Scale Convolutional Attention for Multi-Class Industrial Anomaly Detection
Li, X., Guan, Y., Jiang, J., Ye, M., Yan, H., & Zhang, C. (2026) · Eng—Advances in Engineering · doi:10.3390/eng7090480
Zheng, X., Li, M., Tan, K., Liao, Z., Chen, Y., Lin, D., & Zhong, L. (2026) · International Conference on Electronic Information, Computer and Aerospace Remote Sensing (EICARS) · doi:10.1109/eicars71536.2026.11694905
Few-Shot Anomaly Detection on MVTec AD 2: A Systematic Study and a Strong Frozen Baseline
Tu, W. (2026) · IEEE Access · doi:10.1109/access.2026.3733273
Liu, L., & Wei, W. (2026) · Journal of King Saud University - Computer and Information Sciences · doi:10.1007/s44443-026-01286-1
Cleaning Contaminated Reference Sets for Few-Shot Visual Anomaly Detection
Villanueva, S., Soria‐Olivas, E., & Sánchez-Montañés, M. A. (2026) · Electronics · doi:10.3390/electronics15194486
Risk-Calibrated Few-Shot Industrial Anomaly Detection with Human-in-the-Loop Inspection
Kinalioğlu, İ. H. (2026) · Journal of Advanced Research in Natural and Applied Sciences · doi:10.28979/jarnas.1957308
Wang, L., Zhang, Y., Wang, H., Han, L., Jin, Y., Weng, N., & Ding, Y. (2026) · Microelectronics Journal · doi:10.1016/j.mejo.2026.107533
Self-citationPeng, Y., Fu, Y., Li, X., Li, Z., Chen, S., & Ji, S. (2026) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57385.2026.11696969
A reproducible and edge-oriented industrial visual anomaly inspection framework
Verma, O. P., Kashyap, A., Varyani, A., & Chhabra, A. (2026) · The Visual Computer · doi:10.1007/s00371-026-04746-x
MegAD: An Expert in Meta-Learning Guided Few-Shot Anomaly Detection
Li, X., Jing, J., Wu, T., Gao, T. S., & Sheng, Z. (2026) · doi:10.1109/cvprf71909.2026.00252
iCAS: An in-context anomaly segmentation framework for industrial visual inspection
Deng, H., Gao, B.-B., Jiang, X., Liu, Y., Wang, C., & Li, X. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.115085
Structure-Aware Photovoltaic EL Anomaly Detection with Frozen DINOv2
Çalışan, M. (2026) · Sustainability · doi:10.3390/su182010677
Li, X., Li, L., & Shao, X. (2026) · IET Image Processing · doi:10.1049/ipr2.70493
Metadata
- Authors
- Huang, C., Guan, H., Jiang, A., Zhang, Y., Spratling, M., & Wang, Y.-F.
- Affiliations
- National University of Singapore (SG)
- King's College London (GB)
- Shanghai Jiao Tong University (CN)
- Corresponding author
- Zhang, Ya
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Nature Switzerland
- Published
- 2022
- Pages
- 303-319
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W4312298392
- Cited by
- 216
- References
- 39
- Citations by year
- 2026: 60 · 2025: 71 · 2024: 64 · 2023: 21
- FWCI
- 21.57
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Science and Technology Commission of Shanghai Municipality (BP0719010, 18DZ2270700)
- National Key Research and Development Program of China (BP0719010)
- 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 › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- few-shot anomaly detection, image registration, anomaly detection, MVTec
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