Li Fei-Fei

220.4k citations
420 papers · 134.1k · 38 hit papers · h-index 106

Impact in

    • Advanced Image and Video Retrieval Techniques
    • Advanced Neural Network Applications
    • Multimodal Machine Learning Applications
    • Human Pose and Action Recognition
    • Video Surveillance and Tracking Methods
    • Image Retrieval and Classification Techniques
    • Domain Adaptation and Few-Shot Learning
    • Anomaly Detection Techniques and Applications

Papers in

    • Advanced Image and Video Retrieval Techniques 87
    • Human Pose and Action Recognition 69
    • Multimodal Machine Learning Applications 67
    • Image Retrieval and Classification Techniques 46
    • Advanced Neural Network Applications 34
    • Domain Adaptation and Few-Shot Learning 47

Li Fei-Fei

400 papers receiving 129.5k citations

Li Fei-Fei's Hit Papers

Advances, challenges and opportunities in creating data for trustworthy AI 2022 · 325 citations
3250+3+6Years since publication2.0k4.0k6.0k

Peers

Li Fei-Fei
Comparison fields: 5 of 233
  • Computer Vision and Pattern Recognition 89.5k
  • Artificial Intelligence 52.8k
  • Media Technology 9.5k
  • Health Informatics 806
  • Signal Processing 6.8k
Replace Yann LeCun with:
Yann LeCun United States
Jia Deng United States
Andrew Zisserman United Kingdom
Xiangyu Zhang China
Christian Szegedy United States
Ilya Sutskever Canada
Ross Girshick United States
Yoshua Bengio Canada
Vincent Vanhoucke United States
Li Fei-Fei relative to Yann LeCun United States Yann LeCun's profile →
Citations per field
00.5×1.5×
Yann LeCun · 1×
Citations per year

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-authors

The 25 scholars most cited alongside Li Fei-Fei, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Li Fei-Fei Line = papers co-authored together Li Fei-Fei links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 420 papers — load more, or switch the sort, to bring in the rest.

#Work
1
ImageNet: A large-scale hierarchical image database
Hit paper breakdown →
200940486
2
ImageNet Large Scale Visual Recognition Challenge
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201526693
3
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
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20166552
4
Large-Scale Video Classification with Convolutional Neural Networks
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20144734
5
Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations
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20173352
6
Deep visual-semantic alignments for generating image descriptions
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20153224
7
A Bayesian Hierarchical Model for Learning Natural Scene Categories
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20052810
8
Social LSTM: Human Trajectory Prediction in Crowded Spaces
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20162311
9
3D Object Representations for Fine-Grained Categorization
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20132234
10
One-shot learning of object categories
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20062109
11
ImageNet: A large-scale hierarchical image database
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20091904
12
Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories
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20071571
13
Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words
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20081183
14
Progressive Neural Architecture Search
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20181182
15
DeepLog
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20171095
16
CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning
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2017971
17
DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion
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2019741
18
Deep Visual-Semantic Alignments for Generating Image Descriptions
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2016739
19
Image retrieval using scene graphs
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2015722
20
Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation
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2019711

About Li Fei-Fei

Li Fei-Fei is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Computer Networks and Communications, Signal Processing and Control and Systems Engineering, having authored 420 papers that have together received 134.1k indexed citations. Recurring topics across this work include Advanced Image and Video Retrieval Techniques (87 papers), Human Pose and Action Recognition (69 papers), Multimodal Machine Learning Applications (67 papers), Domain Adaptation and Few-Shot Learning (47 papers), Image Retrieval and Classification Techniques (46 papers), Data Management and Algorithms (44 papers), Advanced Database Systems and Queries (35 papers) and Advanced Neural Network Applications (34 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (89.5k citations), Artificial Intelligence (52.8k citations), Media Technology (9.5k citations), Health Informatics (806 citations) and Signal Processing (6.8k citations). Li Fei-Fei has collaborated with scholars based in United States, China and Hong Kong. Frequent co-authors include Jia Deng, Li-Jia Li, Richard Socher, Kai Li, Wei Dong, Andrej Karpathy, Jonathan Krause, Pietro Perona, Michael S. Bernstein and Alexandre Alahi. Their work appears in journals such as Proceedings of the VLDB Endowment, Journal of Vision, IEEE Transactions on Knowledge and Data Engineering, International Journal of Computer Vision and IEEE Transactions on Pattern Analysis and Machine Intelligence.

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.

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