Li Fei-Fei

217.0k total citations · 29 hit papers
368 papers, 106.7k citations indexed

About

Li Fei-Fei is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Computer Networks and Communications. According to data from OpenAlex, Li Fei-Fei has authored 368 papers receiving a total of 106.7k indexed citations (citations by other indexed papers that have themselves been cited), including 186 papers in Computer Vision and Pattern Recognition, 145 papers in Artificial Intelligence and 50 papers in Computer Networks and Communications. Recurrent topics in Li Fei-Fei's work include Advanced Image and Video Retrieval Techniques (73 papers), Multimodal Machine Learning Applications (57 papers) and Human Pose and Action Recognition (53 papers). Li Fei-Fei is often cited by papers focused on Advanced Image and Video Retrieval Techniques (73 papers), Multimodal Machine Learning Applications (57 papers) and Human Pose and Action Recognition (53 papers). Li Fei-Fei collaborates with scholars based in United States, China and Hong Kong. Li Fei-Fei's co-authors include Jia Deng, Li-Jia Li, Richard Socher, Wei Dong, Kai Li, Andrej Karpathy, Jonathan Krause, Pietro Perona, Michael S. Bernstein and Olga Russakovsky and has published in prestigious journals such as Nature, Science and New England Journal of Medicine.

In The Last Decade

Li Fei-Fei

349 papers receiving 102.8k citations

Hit Papers

ImageNet: A large-scale hierarchical image database 2005 2026 2012 2019 2009 2015 2014 2017 2015 10.0k 20.0k 30.0k

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Li Fei-Fei United States 93 69.3k 43.3k 7.3k 7.1k 5.7k 368 106.7k
Ilya Sutskever Canada 22 36.1k 0.5× 41.1k 1.0× 6.6k 0.9× 5.5k 0.8× 5.3k 0.9× 39 95.2k
Yann LeCun United States 67 47.9k 0.7× 48.5k 1.1× 9.5k 1.3× 8.7k 1.2× 8.7k 1.5× 192 133.0k
Alex Krizhevsky Canada 7 31.3k 0.5× 24.9k 0.6× 6.1k 0.8× 5.3k 0.7× 4.7k 0.8× 7 70.5k
Trevor Darrell United States 84 54.3k 0.8× 24.0k 0.6× 5.2k 0.7× 7.5k 1.0× 4.9k 0.9× 389 78.9k
Xiangyu Zhang China 48 87.3k 1.3× 51.8k 1.2× 15.7k 2.1× 16.4k 2.3× 10.9k 1.9× 201 162.2k
Ross Girshick United States 44 97.1k 1.4× 35.4k 0.8× 8.6k 1.2× 15.4k 2.2× 7.4k 1.3× 67 141.6k
Karen Simonyan United States 23 34.0k 0.5× 21.1k 0.5× 5.6k 0.8× 5.6k 0.8× 3.4k 0.6× 49 63.3k
Andrew Zisserman United Kingdom 110 99.8k 1.4× 34.0k 0.8× 7.6k 1.0× 14.8k 2.1× 6.7k 1.2× 439 137.0k
Jia Deng United States 32 40.9k 0.6× 25.2k 0.6× 6.2k 0.9× 5.3k 0.7× 3.2k 0.6× 61 65.0k

Countries citing papers authored by Li Fei-Fei

Since Specialization
Citations

This map shows the geographic impact of Li Fei-Fei's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Li Fei-Fei with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Li Fei-Fei more than expected).

Fields of papers citing papers by Li Fei-Fei

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Li Fei-Fei. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Li Fei-Fei. The network helps show where Li Fei-Fei may publish in the future.

Co-authorship network of co-authors of Li Fei-Fei

This figure shows the co-authorship network connecting the top 25 collaborators of Li Fei-Fei. A scholar is included among the top collaborators of Li Fei-Fei based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Li Fei-Fei. Li Fei-Fei is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
2.
Wang, Yixuan, Yunzhu Li, Katherine Driggs-Campbell, Li Fei-Fei, & Jiajun Wu. (2023). Dynamic-Resolution Model Learning for Object Pile Manipulation. 5 indexed citations
3.
Fei-Fei, Li, Hans Meine, Liliana Caldeira, et al.. (2023). Distributed Privacy-Preserving Data Analysis in NFDI4Health With the Personal Health Train. 1. 1 indexed citations
5.
Luo, Zelun, et al.. (2021). MOMA: Multi-Object Multi-Actor Activity Parsing. Neural Information Processing Systems. 34. 7 indexed citations
6.
Wang, Yunbo, et al.. (2019). Eidetic 3D LSTM: A Model for Video Prediction and Beyond. International Conference on Learning Representations. 111 indexed citations
7.
Fang, Kuan, Yuke Zhu, Animesh Garg, Silvio Savarese, & Li Fei-Fei. (2019). Dynamics Learning with Cascaded Variational Inference for Multi-Step Manipulation.. 42–52. 5 indexed citations
8.
Zhou, Sharon, et al.. (2019). HYPE: A Benchmark for Human eYe Perceptual Evaluation of Generative Models. Neural Information Processing Systems. 32. 3444–3456. 10 indexed citations
9.
Liu, Bingbin, Michelle Guo, Edward Chou, et al.. (2018). 3D Point Cloud-Based Visual Prediction of ICU Mobility Care Activities.. 17–29. 5 indexed citations
10.
Haque, Albert, Michelle Guo, Alexandre Alahi, et al.. (2017). Towards Vision-Based Smart Hospitals: A System for Tracking and Monitoring Hand Hygiene Compliance. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 75–87. 7 indexed citations
11.
Luo, Zelun, et al.. (2017). Graph Distillation for Action Detection with Privileged Information.. arXiv (Cornell University). 2 indexed citations
12.
Luo, Zelun, Yuliang Zou, Judy Hoffman, & Li Fei-Fei. (2017). Label efficient learning of transferable representations across domains and tasks. Neural Information Processing Systems. 30. 164–176. 64 indexed citations
13.
Jiang, Lu, Zhengyuan Zhou, Thomas Leung, Li-Jia Li, & Li Fei-Fei. (2017). MentorNet: Regularizing Very Deep Neural Networks on Corrupted Labels. arXiv (Cornell University). 21 indexed citations
14.
Shi, Jiaxin, et al.. (2016). Fast and concurrent RDF queries with RDMA-based distributed graph exploration. Operating Systems Design and Implementation. 317–332. 61 indexed citations
15.
Russakovsky, Olga, Jia Deng, Hao Su, et al.. (2015). ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision. 115(3). 211–252. 23680 indexed citations breakdown →
16.
Deng, Jia, Wei Dong, Richard Socher, et al.. (2009). ImageNet: A large-scale hierarchical image database. 2009 IEEE Conference on Computer Vision and Pattern Recognition. 248–255. 35367 indexed citations breakdown →
17.
Fei-Fei, Li. (2009). Study on changes of sensory, physical and chemical properties and the total number of colonies, and the mutual relationship between them during meat storage. Food Science and Technology International. 1 indexed citations
18.
Yao, Bangpeng, Dirk B. Walther, Diane M. Beck, & Li Fei-Fei. (2009). Hierarchical Mixture of Classification Experts Uncovers Interactions between Brain Regions. Neural Information Processing Systems. 22. 2178–2186. 12 indexed citations
19.
Papadimitriou, Spiros, Li Fei-Fei, George Kollios, & Philip S. Yu. (2007). Time series compressibility and privacy. Very Large Data Bases. 459–470. 74 indexed citations
20.
Fei-Fei, Li, Rob Fergus, & Pietro Perona. (2003). A Bayesian Approach to Unsupervised One-Shot Learning of Object Categories. 256 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

Explore authors with similar magnitude of impact

Rankless by CCL
2026