H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 76 Citations 22,982 406 World Ranking 569 National Ranking 349

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Statistics

Yun Fu mostly deals with Artificial intelligence, Pattern recognition, Machine learning, Feature extraction and Facial recognition system. His research investigates the link between Artificial intelligence and topics such as Computer vision that cross with problems in Classifier. His Pattern recognition research includes elements of Contextual image classification, Graph embedding and Residual.

His work deals with themes such as Representation and Natural language processing, which intersect with Machine learning. The concepts of his Feature extraction study are interwoven with issues in Image resolution, Principal component analysis and Hidden Markov model. His studies in Facial recognition system integrate themes in fields like Image processing, Regression analysis, Nonlinear dimensionality reduction and Pattern recognition.

His most cited work include:

  • Residual Dense Network for Image Super-Resolution (925 citations)
  • Image Super-Resolution Using Very Deep Residual Channel Attention Networks (902 citations)
  • Age Synthesis and Estimation via Faces: A Survey (565 citations)

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

His primary areas of investigation include Artificial intelligence, Pattern recognition, Machine learning, Discriminative model and Computer vision. His study in Feature extraction, Facial recognition system, Subspace topology, Cluster analysis and Contextual image classification is done as part of Artificial intelligence. His Pattern recognition study incorporates themes from Image, Feature and Robustness.

In most of his Machine learning studies, his work intersects topics such as Classifier. His research combines Deep learning and Discriminative model. Yun Fu is interested in Pose, which is a field of Computer vision.

He most often published in these fields:

  • Artificial intelligence (79.44%)
  • Pattern recognition (36.73%)
  • Machine learning (32.14%)

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

  • Artificial intelligence (79.44%)
  • Pattern recognition (36.73%)
  • Machine learning (32.14%)

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

Yun Fu spends much of his time researching Artificial intelligence, Pattern recognition, Machine learning, Computer vision and Artificial neural network. All of his Artificial intelligence and Deep learning, Benchmark, Feature, Face and RGB color model investigations are sub-components of the entire Artificial intelligence study. His work carried out in the field of Pattern recognition brings together such families of science as Feature, Image compression and Image.

The Machine learning study combines topics in areas such as Contextual image classification, Matrix decomposition and Focus. When carried out as part of a general Computer vision research project, his work on Feature extraction and Pyramid is frequently linked to work in Diversity, therefore connecting diverse disciplines of study. Yun Fu studied Feature extraction and Imputation that intersect with Cluster analysis.

Between 2019 and 2021, his most popular works were:

  • Residual Dense Network for Image Restoration. (109 citations)
  • TDAN: Temporally-Deformable Alignment Network for Video Super-Resolution (72 citations)
  • Rethinking Classification and Localization for Object Detection (29 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, Pattern recognition, Feature extraction, Benchmark and Feature. His Artificial intelligence study combines topics in areas such as Machine learning and Computer vision. His Machine learning research includes themes of Matrix decomposition and Modality.

His Classifier study in the realm of Pattern recognition connects with subjects such as Convexity. Yun Fu has included themes like Object detection and Minimum bounding box in his Feature extraction study. His Benchmark research is multidisciplinary, incorporating elements of State and Data science.

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

Residual Dense Network for Image Super-Resolution

Yulun Zhang;Yapeng Tian;Yu Kong;Bineng Zhong.
computer vision and pattern recognition (2018)

1012 Citations

Image Super-Resolution Using Very Deep Residual Channel Attention Networks

Yulun Zhang;Kunpeng Li;Kai Li;Lichen Wang.
european conference on computer vision (2018)

967 Citations

Age Synthesis and Estimation via Faces: A Survey

Yun Fu;Guodong Guo;T S Huang.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2010)

714 Citations

Image-Based Human Age Estimation by Manifold Learning and Locally Adjusted Robust Regression

Guodong Guo;Yun Fu;C.R. Dyer;T.S. Huang.
IEEE Transactions on Image Processing (2008)

696 Citations

Learning With $ll ^{1}$ -Graph for Image Analysis

Bin Cheng;Jianchao Yang;Shuicheng Yan;Yun Fu.
IEEE Transactions on Image Processing (2010)

652 Citations

Human age estimation using bio-inspired features

Guodong Guo;Guowang Mu;Yun Fu;Thomas S Huang.
computer vision and pattern recognition (2009)

576 Citations

Human Age Estimation With Regression on Discriminative Aging Manifold

Yun Fu;T.S. Huang.
IEEE Transactions on Multimedia (2008)

493 Citations

Deep Collaborative Filtering via Marginalized Denoising Auto-encoder

Sheng Li;Jaya Kawale;Yun Fu.
conference on information and knowledge management (2015)

352 Citations

Analysis of EEG Signals and Facial Expressions for Continuous Emotion Detection

Mohammad Soleymani;Sadjad Asghari-Esfeden;Yun Fu;Maja Pantic.
IEEE Transactions on Affective Computing (2016)

332 Citations

Generalized Transfer Subspace Learning Through Low-Rank Constraint

Ming Shao;Dmitry Kit;Yun Fu.
International Journal of Computer Vision (2014)

256 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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Top Scientists Citing Yun Fu

Dacheng Tao

Dacheng Tao

University of Sydney

Publications: 76

Shuicheng Yan

Shuicheng Yan

National University of Singapore

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Jiwen Lu

Jiwen Lu

Tsinghua University

Publications: 60

Xuelong Li

Xuelong Li

Northwestern Polytechnical University

Publications: 51

Thomas S. Huang

Thomas S. Huang

University of Illinois at Urbana-Champaign

Publications: 48

Xiao-Yuan Jing

Xiao-Yuan Jing

Wuhan University

Publications: 43

Ling Shao

Ling Shao

Inception Institute of Artificial Intelligence

Publications: 38

Xinbo Gao

Xinbo Gao

Chongqing University of Posts and Telecommunications

Publications: 37

Shiguang Shan

Shiguang Shan

Chinese Academy of Sciences

Publications: 37

Xilin Chen

Xilin Chen

Institute Of Computing Technology

Publications: 37

Meng Wang

Meng Wang

Hefei University of Technology

Publications: 36

Wangmeng Zuo

Wangmeng Zuo

Harbin Institute of Technology

Publications: 35

Zhao Zhang

Zhao Zhang

Hefei University of Technology

Publications: 33

Radu Timofte

Radu Timofte

ETH Zurich

Publications: 32

Qi Tian

Qi Tian

Huawei Technologies (China)

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