Subfield · Computer Vision and Pattern Recognition
Most cited papers in Computer Vision and Pattern Recognition
What are the most cited papers in Computer Vision and Pattern Recognition?
The most cited paper in Computer Vision and Pattern Recognition is "Deep Residual Learning for Image Recognition" (Kaiming He, Xiangyu Zhang, Shaoqing Ren et al., 2016), with 229,585 citations. Next come "Random Forests" (132,956) and "U-Net: Convolutional Networks for Biomedical Image Segmentation" (92,751).
Showing 50 of 97 papers. Every paper with more than 13,152 citations is listed.
| # | Paper | ||
|---|---|---|---|
| 1 | Deep Residual Learning for Image Recognition Kaiming He, Xiangyu Zhang, Shaoqing Ren et al. | Jun 2016 | 229,585 |
| 2 | Random Forests Leo Breiman · Machine Learning | Oct 2001 | 132,956 |
| 3 | U-Net: Convolutional Networks for Biomedical Image Segmentation Olaf Ronneberger, Philipp F. Fischer, Thomas Brox · Lecture notes in computer science | 2015 | 92,751 |
| 4 | Deep learning Yann LeCun, Yoshua Bengio, Geoffrey E. Hinton · Nature | 26 May 2015 | 84,939 |
| 5 | Exploiting Generative AI to Scale up Intelligent Tutoring Systems Jakubův, Jan, Chvalovský, Karel, Goertzel, Zarathustra et al. · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) | 2023 | 79,061 |
| 6 | ImageNet classification with deep convolutional neural networks Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton · Communications of the ACM | 24 May 2017 | 75,634 |
| 7 | Very Deep Convolutional Networks for Large-Scale Image Recognition Karen Simonyan, Andrew Zisserman · arXiv (Cornell University) | 4 Sep 2014 | 75,434 |
| 8 | ImageNet: A large-scale hierarchical image database Jia Hao Deng, Wei Dong, Richard Socher et al. · 2009 IEEE Conference on Computer Vision and Pattern Recognition | Jun 2009 | 63,518 |
| 9 | Gradient-based learning applied to document recognition Yann LeCun, Léon Bottou, Yoshua Bengio et al. · Proceedings of the IEEE | 1998 | 59,921 |
| 10 | Image quality assessment: from error visibility to structural similarity Zhou Wang, Alan Conrad Bovik, Hamid R. Sheikh et al. · IEEE Transactions on Image Processing | Apr 2004 | 58,409 |
| 11 | Distinctive Image Features from Scale-Invariant Keypoints David G. Lowe · International Journal of Computer Vision | 2 Jun 2004 | 56,041 |
| 12 | Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks Shaoqing Ren, Kaiming He, Ross Girshick et al. · IEEE Transactions on Pattern Analysis and Machine Intelligence | 6 Jun 2016 | 55,999 |
| 13 | Going deeper with convolutions Christian Szegedy, Wei Liu, Yangqing Jia et al. | Jun 2015 | 47,422 |
| 14 | Densely Connected Convolutional Networks Gao Ming Huang, Zhuang Liu, Laurens van der Maaten et al. | Jul 2017 | 45,976 |
| 15 | Microsoft COCO: Common Objects in Context Tsung-Yi Lin, Michael Maire, Serge Belongie et al. · Lecture notes in computer science | 2014 | 43,802 |
| 16 | LIBSVM Chih-Chung Chang, Chih‐Jen Lin · ACM Transactions on Intelligent Systems and Technology | Apr 2011 | 41,500 |
| 17 | Support-vector networks Corinna Cortes, Vladimir N. Vapnik · Machine Learning | Sep 1995 | 41,211 |
| 18 | ImageNet Large Scale Visual Recognition Challenge Olga Russakovsky, Jia Hao Deng, Hao Su et al. · International Journal of Computer Vision | 10 Apr 2015 | 40,843 |
| 19 | Fully convolutional networks for semantic segmentation Jonathan Long, Evan Shelhamer, Trevor J. Darrell | Jun 2015 | 37,703 |
| 20 | Swin Transformer: Hierarchical Vision Transformer using Shifted Windows Ze Liu, Yutong Lin, Yue Cao et al. · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) | Oct 2021 | 32,838 |
| 21 | Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation Ross Girshick, Jeff Donahue, Trevor J. Darrell et al. | Jun 2014 | 32,284 |
| 22 | Histograms of Oriented Gradients for Human Detection Navneet Dalal, Bill Triggs | 27 Jul 2005 | 32,222 |
| 23 | Rethinking the Inception Architecture for Computer Vision Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe et al. | Jun 2016 | 31,624 |
| 24 | SSD: Single Shot MultiBox Detector Wei Liu, Dragomir Anguelov, Dumitru Erhan et al. | 16 Jan 2016 | 30,509 |
| 25 | Squeeze-and-Excitation Networks Jie Hu, Li Shen, Gang Zheng Sun | Jun 2018 | 30,332 |
| 26 | Feature Pyramid Networks for Object Detection Tsung-Yi Lin, Piotr Dollár, Ross Girshick et al. | Jul 2017 | 29,985 |
| 27 | Mask R-CNN Kaiming He, Georgia Gkioxari, Piotr Dollár et al. | Oct 2017 | 29,752 |
| 28 | A Computational Approach to Edge Detection John Canny · IEEE Transactions on Pattern Analysis and Machine Intelligence | Nov 1986 | 29,563 |
| 29 | Fast R-CNN Ross Girshick | Dec 2015 | 28,569 |
| 30 | Focal Loss for Dense Object Detection Tsung-Yi Lin, Priya Goyal, Ross Girshick et al. | Oct 2017 | 27,432 |
| 31 | MobileNetV2: Inverted Residuals and Linear Bottlenecks Mark Sandler, Andrew Howard, Menglong Zhu et al. | Jun 2018 | 26,808 |
| 32 | Random sample consensus Martin A. Fischler, Robert C. Bolles · Communications of the ACM | Jun 1981 | 26,036 |
| 33 | Learning Multiple Layers of Features from Tiny Images Alex Krizhevsky | 2024 | 25,341 |
| 34 | CBAM: Convolutional Block Attention Module Sanghyun Woo, Jongchan Park, Joon‐Young Lee et al. · Lecture notes in computer science | 2018 | 23,707 |
| 35 | Compressed sensing David L. Donoho · IEEE Transactions on Information Theory | Apr 2006 | 23,533 |
| 36 | Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Kumar Das et al. | Oct 2017 | 23,064 |
| 37 | Textural Features for Image Classification Robert M. Haralick, K. Shanmugam, Its'Hak Dinstein · IEEE Transactions on Systems Man and Cybernetics | Nov 1973 | 22,878 |
| 38 | DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos et al. · IEEE Transactions on Pattern Analysis and Machine Intelligence | 27 Apr 2017 | 22,754 |
| 39 | Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks Jun-Yan Zhu, Taesung Park, Phillip Isola et al. | Oct 2017 | 22,468 |
| 40 | An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov et al. · arXiv (Cornell University) | 22 Oct 2020 | 22,044 |
| 41 | Pattern Recognition and Machine Learning Nasser M. Nasrabadi · Journal of Electronic Imaging | 2007 | 22,033 |
| 42 | Silhouettes: A graphical aid to the interpretation and validation of cluster analysis Peter J. Rousseeuw · Journal of Computational and Applied Mathematics | Nov 1987 | 21,780 |
| 43 | Regularization and Variable Selection Via the Elastic Net Hui Jun Zou, Trevor Hastie · Journal of the Royal Statistical Society Series B (Statistical Methodology) | 9 Mar 2005 | 21,757 |
| 44 | GAN（Generative Adversarial Nets） 柴田 淳司 · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics | 15 Oct 2017 | 21,691 |
| 45 | A theory for multiresolution signal decomposition: the wavelet representation Stéphane Mallat · IEEE Transactions on Pattern Analysis and Machine Intelligence | Jul 1989 | 21,434 |
| 46 | Reducing the Dimensionality of Data with Neural Networks Geoffrey E. Hinton, Ruslan Salakhutdinov · Science | 27 Jul 2006 | 21,368 |
| 47 | Software survey: VOSviewer, a computer program for bibliometric mapping Nees Jan van Eck, Ludo Waltman · Scientometrics | 30 Dec 2009 | 21,265 |
| 48 | The Pascal Visual Object Classes (VOC) Challenge Mark Everingham, Luc Van Gool, Christopher K. I. Williams et al. · International Journal of Computer Vision | 8 Sep 2009 | 20,137 |
| 49 | Xception: Deep Learning with Depthwise Separable Convolutions François Chollet | Jul 2017 | 19,596 |
| 50 | YOLO9000: Better, Faster, Stronger Joseph Redmon, Ali Farhadi | Jul 2017 | 19,320 |
Citations are OpenAlex's own counts, from its 2026-09-23 snapshot, so the ranking can differ from Google Scholar or Web of Science. A paper is listed for a field when OpenAlex files it under one of the field's topics; retracted papers and papers whose citation count cannot be traced to citing works are left out, and a preprint and the article it became are shown once. Release dates are OpenAlex's: where a source gave only a month or a year, that is what is shown. How the lists are chosen.