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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Where's Wally Now? Deep Generative and Discriminative Embeddings for Novelty Detection
Philippe Burlina, Neil Joshi, I-Jeng Wang
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019), pp. 11499-11508
Closed Access | Times Cited: 49

Showing 1-25 of 49 citing articles:

Uninformed Students: Student-Teacher Anomaly Detection With Discriminative Latent Embeddings
Paul Bergmann, Michael Fauser, David Sattlegger, et al.
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020)
Open Access | Times Cited: 556
Multiresolution Knowledge Distillation for Anomaly Detection
Mohammadreza Salehi, Niousha Sadjadi, Soroosh Baselizadeh, et al.
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2021)
Open Access | Times Cited: 356
Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning
Yu Tian, Guansong Pang, Yuanhong Chen, et al.
2021 IEEE/CVF International Conference on Computer Vision (ICCV) (2021), pp. 4955-4966
Open Access | Times Cited: 281
The MVTec Anomaly Detection Dataset: A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection
Paul Bergmann, Kilian Batzner, Michael Fauser, et al.
International Journal of Computer Vision (2021) Vol. 129, Iss. 4, pp. 1038-1059
Open Access | Times Cited: 275
Beyond Dents and Scratches: Logical Constraints in Unsupervised Anomaly Detection and Localization
Paul Bergmann, Kilian Batzner, Michael Fauser, et al.
International Journal of Computer Vision (2022) Vol. 130, Iss. 4, pp. 947-969
Open Access | Times Cited: 86
Unsupervised anomaly segmentation via deep feature reconstruction
Yong Shi, Jie Yang, Zhiquan Qi
Neurocomputing (2020) Vol. 424, pp. 9-22
Open Access | Times Cited: 100
Student-Teacher Feature Pyramid Matching for Anomaly Detection
Guodong Wang, Shumin Han, Errui Ding, et al.
arXiv (Cornell University) (2021)
Open Access | Times Cited: 81
Anomaly Detection in 3D Point Clouds using Deep Geometric Descriptors
Paul Bergmann, David Sattlegger
2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) (2023), pp. 2612-2622
Open Access | Times Cited: 39
Large margin distribution multi-class supervised novelty detection
Fa Zhu, Wenjie Zhang, Xingchi Chen, et al.
Expert Systems with Applications (2023) Vol. 224, pp. 119937-119937
Closed Access | Times Cited: 30
Is Your Kettle Smarter Than a Hacker? A Scalable Tool for Assessing Replay Attack Vulnerabilities on Consumer IoT Devices
Sara Lazzaro, Vincenzo De Angelis, Anna Maria Mandalari, et al.
(2024)
Open Access | Times Cited: 10
Student-Teacher Feature Pyramid Matching for Unsupervised Anomaly Detection
Guodong Wang, Shumin Han, Errui Ding, et al.
arXiv (Cornell University) (2021)
Closed Access | Times Cited: 44
DFR: Deep Feature Reconstruction for Unsupervised Anomaly Segmentation.
Jie Yang, Yong Shi, Zhiquan Qi
arXiv (Cornell University) (2020)
Closed Access | Times Cited: 41
Unsupervised Anomaly Segmentation Via Multilevel Image Reconstruction and Adaptive Attention-Level Transition
Yi Yan, Deming Wang, Guangliang Zhou, et al.
IEEE Transactions on Instrumentation and Measurement (2021) Vol. 70, pp. 1-12
Open Access | Times Cited: 39
Deep learning based anomaly detection in real-time video
Ahmed Elmetwally, Reem El-Deeb, Samir Elmougy
Multimedia Tools and Applications (2024)
Open Access | Times Cited: 5
Detecting Anomalies in Retinal Diseases Using Generative, Discriminative, and Self-supervised Deep Learning
Philippe Burlina, William Paul, T. Y. Alvin Liu, et al.
JAMA Ophthalmology (2021) Vol. 140, Iss. 2, pp. 185-185
Open Access | Times Cited: 27
Semi-supervised visual anomaly detection based on convolutional autoencoder and transfer learning
Jamal Saeedi, Alessandro Giusti
Machine Learning with Applications (2023) Vol. 11, pp. 100451-100451
Open Access | Times Cited: 12
A black-box assessment of authentication and reliability in consumer IoT Devices
Sara Lazzaro, Vincenzo De Angelis, Anna Maria Mandalari, et al.
Pervasive and Mobile Computing (2025), pp. 102045-102045
Open Access
Anomaly localization by modeling perceptual features
David Dehaene, Pierre Eline
arXiv (Cornell University) (2020)
Open Access | Times Cited: 27
The ROAD to discovery: Machine-learning-driven anomaly detection in radio astronomy spectrograms
Michael Mesarcik, Albert‐Jan Boonstra, M. Iacobelli, et al.
Astronomy and Astrophysics (2023) Vol. 680, pp. A74-A74
Open Access | Times Cited: 6
Visual Anomaly Detection for Images: A Survey
Jie Yang, Ruijie Xu, Zhiquan Qi, et al.
arXiv (Cornell University) (2021)
Open Access | Times Cited: 15
AD-Graph: Weakly Supervised Anomaly Detection Graph Neural Network
Waseem Ullah, Tanveer Hussain, Fath U Min Ullah, et al.
International Journal of Intelligent Systems (2023) Vol. 2023, pp. 1-12
Open Access | Times Cited: 6
Inter-Realization Channels: Unsupervised Anomaly Detection Beyond One-Class Classification
Declan McIntosh, Alexandra Branzan Albu
2021 IEEE/CVF International Conference on Computer Vision (ICCV) (2023), pp. 6262-6272
Closed Access | Times Cited: 6
SAE-PPL: Self-guided attention encoder with prior knowledge-guided pseudo labels for weakly supervised video anomaly detection
Tang Jun, Zhentao Wang, Guanyu Hao, et al.
Journal of Visual Communication and Image Representation (2023) Vol. 97, pp. 103967-103967
Closed Access | Times Cited: 5
Unsupervised Anomaly Detection with Multi-scale Interpolated Gaussian Descriptors
Yuanhong Chen, Yu Tian, Guansong Pang, et al.
arXiv (Cornell University) (2021)
Closed Access | Times Cited: 11
Interleaving One-Class and Weakly-Supervised Models with Adaptive Thresholding for Unsupervised Video Anomaly Detection
Yongwei Nie, Hao Huang, Chengjiang Long, et al.
Lecture notes in computer science (2024), pp. 449-467
Closed Access | Times Cited: 1
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