D-Index & Metrics Best Publications

D-Index & Metrics

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 54 Citations 13,144 211 World Ranking 2309 National Ranking 229

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Computer vision

His primary areas of study are Artificial intelligence, Pattern recognition, Machine learning, Background subtraction and Algorithm. His study on Artificial intelligence is mostly dedicated to connecting different topics, such as Computer vision. The various areas that Deyu Meng examines in his Pattern recognition study include Noise reduction, Iterative reconstruction and Noise.

As a part of the same scientific study, he usually deals with the Machine learning, concentrating on Weighting and frequently concerns with Multimedia search, Heuristic, Continuous function, Classifier and Training set. Deyu Meng combines subjects such as Robust principal component analysis, Norm and Mathematical optimization with his study of Background subtraction. In his work, Iterated function, Thresholding and Contextual image classification is strongly intertwined with Image restoration, which is a subfield of Algorithm.

His most cited work include:

  • Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising (2693 citations)
  • Weighted Nuclear Norm Minimization and Its Applications to Low Level Vision (315 citations)
  • Co-Saliency Detection via a Self-Paced Multiple-Instance Learning Framework (296 citations)

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

Deyu Meng focuses on Artificial intelligence, Pattern recognition, Machine learning, Deep learning and Algorithm. His Artificial intelligence study frequently draws connections to other fields, such as Computer vision. Deyu Meng has included themes like Regularization, Noise reduction and Feature in his Pattern recognition study.

His work on Semi-supervised learning, Leverage and Supervised learning as part of general Machine learning research is frequently linked to Self paced, bridging the gap between disciplines. His studies deal with areas such as Subspace topology, Mathematical optimization, Iterative reconstruction and Tensor as well as Algorithm. His studies in Subspace topology integrate themes in fields like Mixture model and Optimization problem.

He most often published in these fields:

  • Artificial intelligence (82.16%)
  • Pattern recognition (44.98%)
  • Machine learning (22.30%)

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

  • Artificial intelligence (82.16%)
  • Pattern recognition (44.98%)
  • Deep learning (16.73%)

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

Artificial intelligence, Pattern recognition, Deep learning, Machine learning and Image are his primary areas of study. His Artificial intelligence study combines topics in areas such as Generalization and Computer vision. His biological study spans a wide range of topics, including Regularization, Noise reduction and Residual.

Noise is closely connected to Image restoration in his research, which is encompassed under the umbrella topic of Deep learning. His Machine learning research integrates issues from Generative model and Benchmark. Many of his research projects under Image are closely connected to Field with Field, tying the diverse disciplines of science together.

Between 2019 and 2021, his most popular works were:

  • Hyperspectral Image Classification With Convolutional Neural Network and Active Learning (30 citations)
  • A Model-Driven Deep Neural Network for Single Image Rain Removal (21 citations)
  • A Fast Adaptive k-means with No Bounds. (16 citations)

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

  • Artificial intelligence
  • Machine learning
  • Computer vision

His scientific interests lie mostly in Artificial intelligence, Pattern recognition, Deep learning, Image and Hyperspectral imaging. As a member of one scientific family, Deyu Meng mostly works in the field of Artificial intelligence, focusing on Machine learning and, on occasion, Classifier. His Pattern recognition study combines topics from a wide range of disciplines, such as Artificial neural network, Noise reduction and Image resolution.

His work deals with themes such as Ground truth, Voxel and Atlas, which intersect with Deep learning. His work on Inpainting as part of general Image study is frequently linked to Scale, therefore connecting diverse disciplines of science. His research investigates the connection with Hyperspectral imaging and areas like Multispectral image which intersect with concerns in Rank, Generative model and Image restoration.

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

Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising

Kai Zhang;Wangmeng Zuo;Yunjin Chen;Deyu Meng.
IEEE Transactions on Image Processing (2017)

2243 Citations

Weighted Nuclear Norm Minimization and Its Applications to Low Level Vision

Shuhang Gu;Qi Xie;Deyu Meng;Wangmeng Zuo.
International Journal of Computer Vision (2017)

406 Citations

Co-Saliency Detection via a Self-Paced Multiple-Instance Learning Framework

Dingwen Zhang;Deyu Meng;Junwei Han.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2017)

402 Citations

Convolutional Sparse Coding for Image Super-Resolution

Shuhang Gu;Wangmeng Zuo;Qi Xie;Deyu Meng.
international conference on computer vision (2015)

299 Citations

Self-paced curriculum learning

Lu Jiang;Deyu Meng;Qian Zhao;Shiguang Shan.
national conference on artificial intelligence (2015)

264 Citations

A Generalized Iterated Shrinkage Algorithm for Non-convex Sparse Coding

Wangmeng Zuo;Deyu Meng;Lei Zhang;Xiangchu Feng.
international conference on computer vision (2013)

252 Citations

Infrared Patch-Image Model for Small Target Detection in a Single Image

Chenqiang Gao;Deyu Meng;Yi Yang;Yongtao Wang.
IEEE Transactions on Image Processing (2013)

252 Citations

Self-Paced Learning with Diversity

Lu Jiang;Deyu Meng;Shoou-I Yu;Zhenzhong Lan.
neural information processing systems (2014)

235 Citations

Decomposable Nonlocal Tensor Dictionary Learning for Multispectral Image Denoising

Yi Peng;Deyu Meng;Zongben Xu;Chenqiang Gao.
computer vision and pattern recognition (2014)

222 Citations

DecideNet: Counting Varying Density Crowds Through Attention Guided Detection and Density Estimation

Jiang Liu;Chenqiang Gao;Deyu Meng;Alexander G. Hauptmann.
computer vision and pattern recognition (2018)

213 Citations

Best Scientists Citing Deyu Meng

Lei Zhang

Lei Zhang

Hong Kong Polytechnic University

Publications: 85

Wangmeng Zuo

Wangmeng Zuo

Harbin Institute of Technology

Publications: 80

Yi Yang

Yi Yang

Zhejiang University

Publications: 59

Ting-Zhu Huang

Ting-Zhu Huang

University of Electronic Science and Technology of China

Publications: 42

Ling Shao

Ling Shao

Inception Institute of Artificial Intelligence

Publications: 41

Alexander G. Hauptmann

Alexander G. Hauptmann

Carnegie Mellon University

Publications: 40

Ming-Hsuan Yang

Ming-Hsuan Yang

University of California, Merced

Publications: 37

Jiaying Liu

Jiaying Liu

Peking University

Publications: 34

Radu Timofte

Radu Timofte

ETH Zurich

Publications: 33

David Zhang

David Zhang

Chinese University of Hong Kong, Shenzhen

Publications: 33

Jocelyn Chanussot

Jocelyn Chanussot

Grenoble Alpes University

Publications: 31

Xin Yuan

Xin Yuan

Nanyang Technological University

Publications: 31

Liang Lin

Liang Lin

Sun Yat-sen University

Publications: 30

Junwei Han

Junwei Han

Northwestern Polytechnical University

Publications: 29

Michael Elad

Michael Elad

Technion – Israel Institute of Technology

Publications: 27

Luc Van Gool

Luc Van Gool

ETH Zurich

Publications: 26

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

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