Most cited papers in Human Pose and Action Recognition
What are the most cited papers in Human Pose and Action Recognition?
The most cited paper in Human Pose and Action Recognition is "Densely Connected Convolutional Networks" (Gao Ming Huang, Zhuang Liu, Laurens van der Maaten et al., 2017), with 45,976 citations. Next come "Histograms of Oriented Gradients for Human Detection" (32,222) and "Mask R-CNN" (29,752).
Showing 50 of 98 papers. Every paper with more than 1,752 citations is listed.
| # | Paper | ||
|---|---|---|---|
| 1 | Densely Connected Convolutional Networks Gao Ming Huang, Zhuang Liu, Laurens van der Maaten et al. | Jul 2017 | 45,976 |
| 2 | Histograms of Oriented Gradients for Human Detection Navneet Dalal, Bill Triggs | 27 Jul 2005 | 32,222 |
| 3 | Mask R-CNN Kaiming He, Georgia Gkioxari, Piotr Dollár et al. | Oct 2017 | 29,752 |
| 4 | Non-local Neural Networks Xiaolong Wang, Ross Girshick, Abhinav Gupta et al. | Jun 2018 | 11,465 |
| 5 | Learning Spatiotemporal Features with 3D Convolutional Networks Du Tran, Lubomir Bourdev, Rob Fergus et al. | Dec 2015 | 9,921 |
| 6 | Realtime Multi-person 2D Pose Estimation Using Part Affinity Fields Zhe Cao, Tomas Simon, Shih-En Wei et al. | Jul 2017 | 7,513 |
| 7 | Show, Attend and Tell: Neural Image Caption Generation with Visual Attention Kelvin Xu, Jimmy Lei Ba, Ryan Kiros et al. · arXiv (Cornell University) | 10 Feb 2015 | 7,513 |
| 8 | Dynamic Graph CNN for Learning on Point Clouds Yue Wang, Yongbin Sun, Ziwei Liu et al. · ACM Transactions on Graphics | 10 Oct 2019 | 7,071 |
| 9 | Recent advances in convolutional neural networks Jiuxiang Gu, Zhenhua Wang, Jason Kuen et al. · Pattern Recognition | 13 Oct 2017 | 6,410 |
| 10 | Large-Scale Video Classification with Convolutional Neural Networks Andrej Karpathy, George D. Toderici, Sanketh Shetty et al. | Jun 2014 | 6,403 |
| 11 | Deep High-Resolution Representation Learning for Human Pose Estimation Ke Sun, Bin Xiao, Dong Liu et al. | Jun 2019 | 5,840 |
| 12 | Two-Stream Convolutional Networks for Action Recognition in Videos Karen Simonyan, Andrew Zisserman · arXiv (Cornell University) | 9 Jun 2014 | 5,362 |
| 13 | Long-term recurrent convolutional networks for visual recognition and description Jeff Donahue, Lisa Anne Hendricks, Sergio Guadarrama et al. | Jun 2015 | 5,351 |
| 14 | Stacked Hourglass Networks for Human Pose Estimation Alejandro Newell, Kaiyu Yang, Jia Hao Deng · Lecture notes in computer science | 2016 | 5,296 |
| 15 | Learning Transferable Visual Models From Natural Language Supervision Alec Radford, Jong Wook Kim, Chris Hallacy et al. · arXiv (Cornell University) | 26 Feb 2021 | 5,294 |
| 16 | OpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields Zhe Cao, Gines Hidalgo, Tomas Simon et al. · IEEE Transactions on Pattern Analysis and Machine Intelligence | 17 Jul 2019 | 5,255 |
| 17 | Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition Sijie Yan, Yuanjun Xiong, Dahua Lin · Proceedings of the AAAI Conference on Artificial Intelligence | 27 Apr 2018 | 5,096 |
| 18 | Simple online and realtime tracking with a deep association metric Nicolai Wojke, Alex Bewley, Dietrich W. R. Paulus | Sep 2017 | 4,989 |
| 19 | Deep High-Resolution Representation Learning for Visual Recognition Jingdong Wang, Ke Sun, Tianheng Cheng et al. · IEEE Transactions on Pattern Analysis and Machine Intelligence | Apr 2020 | 4,860 |
| 20 | CNN Features Off-the-Shelf: An Astounding Baseline for Recognition Ali Sharif Razavian, Hossein Azizpour, Josephine Sullivan et al. | Jun 2014 | 4,392 |
| 21 | Pfinder: real-time tracking of the human body Christopher R. Wren, Ali J. Azarbayejani, Trevor J. Darrell et al. · IEEE Transactions on Pattern Analysis and Machine Intelligence | Jul 1997 | 4,175 |
| 22 | Temporal Segment Networks: Towards Good Practices for Deep Action Recognition Limin Wang, Yuanjun Xiong, Zhe Wang et al. · Lecture notes in computer science | 2016 | 4,010 |
| 23 | HMDB: A large video database for human motion recognition Hilde Kuehne, Hueihan Jhuang, Estíbaliz Garrote et al. | Nov 2011 | 3,990 |
| 24 | SMPL Matthew Loper, Naureen Mahmood, Javier Romero et al. · ACM Transactions on Graphics | 26 Oct 2015 | 3,807 |
| 25 | Human3.6M: Large Scale Datasets and Predictive Methods for 3D Human Sensing in Natural Environments Catalin Ionescu, Dragos Papava, Vlad Olaru et al. · IEEE Transactions on Pattern Analysis and Machine Intelligence | 31 Jan 2014 | 3,786 |
| 26 | SlowFast Networks for Video Recognition Christoph Feichtenhofer, Haoqi Fan, Jitendra Malik et al. | Oct 2019 | 3,782 |
| 27 | VoxNet: A 3D Convolutional Neural Network for real-time object recognition Daniel Maturana, Sebastian Scherer | Sep 2015 | 3,650 |
| 28 | A Closer Look at Spatiotemporal Convolutions for Action Recognition Du Tran, Heng Wang, Lorenzo Torresani et al. | Jun 2018 | 3,637 |
| 29 | Social LSTM: Human Trajectory Prediction in Crowded Spaces Alexandre Alahi, Kratarth Goel, Vignesh Ramanathan et al. | Jun 2016 | 3,626 |
| 30 | Action Recognition with Improved Trajectories Heng Wang, Cordelia Schmid | Dec 2013 | 3,570 |
| 31 | Real-time human pose recognition in parts from single depth images Jamie Shotton, Andrew W Fitzgibbon, Mat Cook et al. | Jun 2011 | 3,551 |
| 32 | Learning realistic human actions from movies Ivan Laptev, Marcin Marszałek, Cordelia Schmid et al. | Jun 2008 | 3,525 |
| 33 | Mask R-CNN Kaiming He, Georgia Gkioxari, Piotr Dollár et al. · IEEE Transactions on Pattern Analysis and Machine Intelligence | 5 Jun 2018 | 3,506 |
| 34 | Deep Learning for Computer Vision: A Brief Review Athanasios S. Voulodimos, Nikolaos D. Doulamis, Anastasios D. Doulamis et al. · Computational Intelligence and Neuroscience | 2018 | 3,387 |
| 35 | DeepPose: Human Pose Estimation via Deep Neural Networks Alexander Toshev, Christian Szegedy | Jun 2014 | 3,331 |
| 36 | Deep CORAL: Correlation Alignment for Deep Domain Adaptation Baochen Sun, Kate Saenko · Lecture notes in computer science | 2016 | 3,324 |
| 37 | Learning patterns of activity using real-time tracking Chris Stauffer, W. Eric L. Grimson · IEEE Transactions on Pattern Analysis and Machine Intelligence | 2000 | 3,248 |
| 38 | Continual lifelong learning with neural networks: A review German Ignacio Parisi, Ronald Kemker, Jose L. Part et al. · Neural Networks | 10 Feb 2019 | 3,189 |
| 39 | Incremental Learning for Robust Visual Tracking David A. Ross, Jongwoo Lim, Ruei-Sung Lin et al. · International Journal of Computer Vision | 16 Aug 2007 | 3,114 |
| 40 | NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis Amir Shahroudy, Jun Liu, Tian-Tsong Ng et al. | Jun 2016 | 3,067 |
| 41 | Recognizing human actions: a local SVM approach Christian Schüldt, Ivan Laptev, Barbara Caputo · Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004. | 2004 | 3,011 |
| 42 | A discriminatively trained, multiscale, deformable part model Pedro F. Felzenszwalb, David McAllester, Deva Ramanan | Jun 2008 | 2,928 |
| 43 | 2D Human Pose Estimation: New Benchmark and State of the Art Analysis Mykhaylo Andriluka, Leonid Pishchulin, Peter Gehler et al. | Jun 2014 | 2,878 |
| 44 | Convolutional Pose Machines Shih-En Wei, Varun Ramakrishna, Takeo Kanade et al. | Jun 2016 | 2,835 |
| 45 | The recognition of human movement using temporal templates Aaron Bobick, James W. Davis · IEEE Transactions on Pattern Analysis and Machine Intelligence | Mar 2001 | 2,820 |
| 46 | Convolutional Two-Stream Network Fusion for Video Action Recognition Christoph Feichtenhofer, Axel J. Pinz, Andrew Zisserman | Jun 2016 | 2,802 |
| 47 | DeepReID: Deep Filter Pairing Neural Network for Person Re-identification Wei Li, Rui Zhao, Tong Xiao et al. | Jun 2014 | 2,776 |
| 48 | Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition Francisco Javier Ordóñez, Daniel Roggen · Sensors | 18 Jan 2016 | 2,769 |
| 49 | ActivityNet: A large-scale video benchmark for human activity understanding Fabian Caba Heilbron, Victor A Escorcia, Bernard Ghanem et al. | Jun 2015 | 2,703 |
| 50 | On Space-Time Interest Points Ivan Laptev · International Journal of Computer Vision | Sep 2005 | 2,693 |
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.