D-Index & Metrics Best Publications
Research.com 2023 Best Female Scientist Award Badge
Computer Science
USA
2023

D-Index & Metrics D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines.

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 122 Citations 160,524 387 World Ranking 73 National Ranking 44
Best female scientists D-index 122 Citations 160,574 390 World Ranking 297 National Ranking 188

Research.com Recognitions

Awards & Achievements

2023 - Research.com Best Female Scientist Award

2023 - Research.com Computer Science in United States Leader Award

2022 - Research.com Best Female Scientist Award

Overview

What is he best known for?

The fields of study Li Fei-Fei is best known for:

  • Artificial intelligence
  • Machine learning
  • Visual cortex

His study connects Categorization and Artificial intelligence. His research on Pattern recognition (psychology) often connects related areas such as Object detection. Li Fei-Fei regularly links together related areas like Pattern recognition (psychology) in his Object detection studies. Computer vision and Machine learning are two areas of study in which he engages in interdisciplinary work. He incorporates Machine learning and Computer vision in his studies. His Object (grammar) study frequently intersects with other fields, such as Cognitive neuroscience of visual object recognition. Cognitive neuroscience of visual object recognition is closely attributed to Object (grammar) in his work. His Image (mathematics) study frequently links to adjacent areas such as Contextual image classification. He frequently studies issues relating to Image (mathematics) and Contextual image classification.

His most cited work include:

  • ImageNet Large Scale Visual Recognition Challenge (26234 citations)
  • Perceptual Losses for Real-Time Style Transfer and Super-Resolution (4825 citations)
  • Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations (2713 citations)

What are the main themes of his work throughout his whole career to date

His Artificial intelligence research covers fields of interest such as Machine learning, Computer vision, Natural language processing, Artificial neural network, Cognitive psychology and Human–computer interaction. Machine learning and Artificial intelligence are two areas of study in which he engages in interdisciplinary work. His research on Programming language frequently connects to adjacent areas such as Set (abstract data type). Set (abstract data type) is closely attributed to Programming language in his study. His Task (project management) study frequently draws parallels with other fields, such as Management. His research on Management often connects related areas such as Task (project management). By researching both Neuroscience and Perception, he produces research that crosses academic boundaries. Li Fei-Fei integrates many fields, such as Perception and Neuroscience, in his works. In his works, he conducts interdisciplinary research on Law and Politics.

Li Fei-Fei most often published in these fields:

  • Artificial intelligence (94.41%)
  • Machine learning (38.51%)
  • Pattern recognition (psychology) (33.54%)

What were the highlights of his more recent work (between 2019-2021)?

  • Artificial intelligence (77.78%)
  • Human–computer interaction (33.33%)
  • Computer vision (27.78%)

In recent works Li Fei-Fei was focusing on the following fields of study:

Li Fei-Fei conducts interdisciplinary study in the fields of Artificial intelligence and Deep learning through his works. In his papers, Li Fei-Fei integrates diverse fields, such as Human–computer interaction and Operating system. He integrates many fields in his works, including Operating system and Human–computer interaction. Li Fei-Fei regularly links together related areas like Segmentation in his Computer vision studies. As part of his studies on Segmentation, he often connects relevant subjects like Computer vision. In his papers, he integrates diverse fields, such as Machine learning and Artificial intelligence. Li Fei-Fei undertakes multidisciplinary studies into Neuroscience and Developmental psychology in his work. He integrates many fields in his works, including Developmental psychology and Neuroscience. His study brings together the fields of Movement disorders and Pathology.

Between 2019 and 2021, his most popular works were:

  • Illuminating the dark spaces of healthcare with ambient intelligence (106 citations)
  • Learning task-oriented grasping for tool manipulation from simulated self-supervision (90 citations)
  • Making Sense of Vision and Touch: Learning Multimodal Representations for Contact-Rich Tasks (63 citations)

In his most recent research, the most cited works focused on:

  • Artificial intelligence
  • Human–computer interaction
  • Systems engineering

His Law study frequently intersects with other fields, such as Health care and Foundation (evidence). As part of his studies on Health care, he often connects relevant subjects like Law. He is involved in relevant fields of research such as Software deployment and Process (computing) in the domain of Operating system. He merges many fields, such as Process (computing) and Operating system, in his writings. In his articles, Li Fei-Fei combines various disciplines, including Artificial intelligence and Data science. Li Fei-Fei conducted interdisciplinary study in his works that combined Data science and Artificial intelligence. Li Fei-Fei conducts interdisciplinary study in the fields of Human–computer interaction and Computer vision through his research. He integrates Computer vision and Multimodal interaction in his research. Li Fei-Fei integrates many fields in his works, including Multimodal interaction and Human–computer interaction.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

ImageNet: A large-scale hierarchical image database

Jia Deng;Wei Dong;Richard Socher;Li-Jia Li.
computer vision and pattern recognition (2009)

38296 Citations

ImageNet Large Scale Visual Recognition Challenge

Olga Russakovsky;Jia Deng;Hao Su;Jonathan Krause.
International Journal of Computer Vision (2015)

29326 Citations

Perceptual Losses for Real-Time Style Transfer and Super-Resolution

Justin Johnson;Alexandre Alahi;Li Fei-Fei.
european conference on computer vision (2016)

6641 Citations

Large-Scale Video Classification with Convolutional Neural Networks

Andrej Karpathy;George Toderici;Sanketh Shetty;Thomas Leung.
computer vision and pattern recognition (2014)

6597 Citations

A Bayesian hierarchical model for learning natural scene categories

L. Fei-Fei;P. Perona.
computer vision and pattern recognition (2005)

4826 Citations

Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories

Li Fei-Fei;R. Fergus;P. Perona.
computer vision and pattern recognition (2004)

4357 Citations

Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories

Li Fei-Fei;Rob Fergus;Pietro Perona.
Computer Vision and Image Understanding (2007)

4315 Citations

Deep visual-semantic alignments for generating image descriptions

Andrej Karpathy;Li Fei-Fei.
computer vision and pattern recognition (2015)

4235 Citations

Deep visual-semantic alignments for generating image descriptions

Andrej Karpathy;Li Fei-Fei.
computer vision and pattern recognition (2015)

3110 Citations

Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations

Ranjay Krishna;Yuke Zhu;Oliver Groth;Justin Johnson.
International Journal of Computer Vision (2017)

2793 Citations

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