H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 114 Citations 150,174 288 World Ranking 74 National Ranking 45

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Statistics

Li Fei-Fei mostly deals with Artificial intelligence, Machine learning, Pattern recognition, Object and Computer vision. His research in Cognitive neuroscience of visual object recognition, Contextual image classification, Visualization, Categorization and Convolutional neural network are components of Artificial intelligence. In his research on the topic of Cognitive neuroscience of visual object recognition, Iterative reconstruction is strongly related with Object detection.

His study in Contextual image classification is interdisciplinary in nature, drawing from both Graphical model, WordNet, Natural language processing and Image retrieval. His Machine learning research is multidisciplinary, relying on both Feature extraction, Caltech 101 and Benchmark. His Object research incorporates elements of Learning object, Bayesian probability and Automatic image annotation.

His most cited work include:

  • ImageNet: A large-scale hierarchical image database (22839 citations)
  • ImageNet Large Scale Visual Recognition Challenge (18266 citations)
  • Perceptual Losses for Real-Time Style Transfer and Super-Resolution (4429 citations)

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

His primary areas of investigation include Artificial intelligence, Machine learning, Pattern recognition, Computer vision and Object. His research on Artificial intelligence often connects related areas such as Natural language processing. His research in Machine learning intersects with topics in Contextual image classification, Feature extraction, Training set and Data mining.

His Pattern recognition research is multidisciplinary, incorporating elements of Recurrent neural network, Image and Feature. Object detection and Pose are among the areas of Computer vision where the researcher is concentrating his efforts. Much of his study explores Object relationship to Representation.

He most often published in these fields:

  • Artificial intelligence (71.46%)
  • Machine learning (25.31%)
  • Pattern recognition (22.83%)

What were the highlights of his more recent work (between 2018-2021)?

  • Artificial intelligence (71.46%)
  • Robot (7.69%)
  • Human–computer interaction (8.93%)

In recent papers he was focusing on the following fields of study:

His primary areas of study are Artificial intelligence, Robot, Human–computer interaction, Machine learning and Representation. Li Fei-Fei has included themes like Computer vision and Pattern recognition in his Artificial intelligence study. His biological study spans a wide range of topics, including Contextual image classification and Categorization.

The concepts of his Robot study are interwoven with issues in Object, Motion, Leverage and Set. His studies deal with areas such as Human behavior, Heuristics, Trajectory and Training set as well as Machine learning. His work carried out in the field of Representation brings together such families of science as Ontology, WordNet, Vocabulary and Root.

Between 2018 and 2021, his most popular works were:

  • Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation (445 citations)
  • DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion (234 citations)
  • Making Sense of Vision and Touch: Self-Supervised Learning of Multimodal Representations for Contact-Rich Tasks (123 citations)

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

  • Artificial intelligence
  • Machine learning
  • Statistics

His primary scientific interests are in Artificial intelligence, Visualization, Deep learning, Machine learning and Task analysis. His studies in Artificial intelligence integrate themes in fields like Visual perception and Pattern recognition. His Pattern recognition research integrates issues from Contextual image classification, Abnormality detection and Triage.

The various areas that Li Fei-Fei examines in his Contextual image classification study include Image resolution, Pascal, Semantic image segmentation and Image retrieval. His Visualization study also includes

  • Robot which intersects with area such as Object, Pose, Computer vision and RGB color model,
  • Representation and related Root, Information retrieval and Vocabulary,
  • Action, which have a strong connection to Vanishing point, Representation, Graph theory and Natural language processing. His research in Machine learning intersects with topics in Path, Human behavior, Heuristics and Trajectory.

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.

Top Publications

ImageNet: A large-scale hierarchical image database

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

21370 Citations

ImageNet Large Scale Visual Recognition Challenge

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

17815 Citations

Large-Scale Video Classification with Convolutional Neural Networks

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

4823 Citations

A Bayesian hierarchical model for learning natural scene categories

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

4455 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 pattern recognition (2004)

3809 Citations

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

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

3641 Citations

Deep visual-semantic alignments for generating image descriptions

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

3110 Citations

Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words

Juan Carlos Niebles;Hongcheng Wang;Li Fei-Fei.
International Journal of Computer Vision (2008)

2089 Citations

One-shot learning of object categories

Li Fei-Fei;R. Fergus;P. Perona.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2006)

1708 Citations

Large-scale Video Classification with Convolutional Neural Networks

Andrej Karpathy;George Toderici;Sanketh Shetty;Thomas Leung.
(2014)

1483 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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