H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 82 Citations 36,611 161 World Ranking 401 National Ranking 8

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Computer vision

His scientific interests lie mostly in Artificial intelligence, Pattern recognition, Convolutional neural network, Machine learning and Computer vision. His research in Robustness, Feature, Deep learning, Face and Segmentation are components of Artificial intelligence. His study in the field of Normalization is also linked to topics like Brightness.

His Convolutional neural network study integrates concerns from other disciplines, such as Algorithm, Image, Parsing and Feature extraction. Many of his research projects under Image are closely connected to Structure with Structure, tying the diverse disciplines of science together. His work carried out in the field of Machine learning brings together such families of science as Inference, Face detection, Pose, Crowd density and Visualization.

His most cited work include:

  • Image Super-Resolution Using Deep Convolutional Networks (3329 citations)
  • Learning a Deep Convolutional Network for Image Super-Resolution (2436 citations)
  • Accelerating the Super-Resolution Convolutional Neural Network (1051 citations)

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

Artificial intelligence, Pattern recognition, Machine learning, Computer vision and Convolutional neural network are his primary areas of study. His Deep learning, Face, Segmentation, Feature and Image investigations are all subjects of Artificial intelligence research. His study in the field of Superresolution also crosses realms of Measure.

His studies deal with areas such as Pascal and Robustness as well as Pattern recognition. His Object, Image resolution and Compression artifact study in the realm of Computer vision interacts with subjects such as Field. Chen Change Loy has researched Convolutional neural network in several fields, including Parsing, Artificial neural network, Inference, Algorithm and Feature extraction.

He most often published in these fields:

  • Artificial intelligence (85.71%)
  • Pattern recognition (31.93%)
  • Machine learning (30.25%)

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

  • Artificial intelligence (85.71%)
  • Machine learning (30.25%)
  • Computer vision (24.37%)

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

Chen Change Loy spends much of his time researching Artificial intelligence, Machine learning, Computer vision, Segmentation and Code. Artificial intelligence is closely attributed to Flow in his work. His Machine learning research incorporates elements of Classifier, Facial recognition system and State.

The concepts of his Code study are interwoven with issues in Object detection, Software engineering and Task. As a part of the same scientific study, Chen Change Loy usually deals with the Deep learning, concentrating on Gesture and frequently concerns with Face. His Image warping research includes themes of Optical flow estimation and Image.

Between 2018 and 2021, his most popular works were:

  • MMDetection: Open MMLab Detection Toolbox and Benchmark. (391 citations)
  • Hybrid Task Cascade for Instance Segmentation (226 citations)
  • EDVR: Video Restoration With Enhanced Deformable Convolutional Networks (194 citations)

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

  • Artificial intelligence
  • Machine learning
  • Computer vision

His primary areas of investigation include Artificial intelligence, Object detection, Feature, Code and Benchmark. Many of his studies on Artificial intelligence involve topics that are commonly interrelated, such as Computer vision. He works mostly in the field of Object detection, limiting it down to concerns involving Segmentation and, occasionally, Leverage and Kernel.

The study incorporates disciplines such as Image resolution, Deblurring, Iterative reconstruction and Pyramid in addition to Code. His biological study spans a wide range of topics, including Codebase, Modality and Software engineering. Chen Change Loy interconnects Deep learning and Image segmentation in the investigation of issues within Feature learning.

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

Image Super-Resolution Using Deep Convolutional Networks

Chao Dong;Chen Change Loy;Kaiming He;Xiaoou Tang.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2016)

3441 Citations

Learning a Deep Convolutional Network for Image Super-Resolution

Chao Dong;Chen Change Loy;Kaiming He;Xiaoou Tang.
european conference on computer vision (2014)

2533 Citations

Accelerating the Super-Resolution Convolutional Neural Network

Chao Dong;Chen Change Loy;Xiaoou Tang.
european conference on computer vision (2016)

1072 Citations

Facial Landmark Detection by Deep Multi-task Learning

Zhanpeng Zhang;Ping Luo;Chen Change Loy;Xiaoou Tang.
european conference on computer vision (2014)

1020 Citations

WIDER FACE: A Face Detection Benchmark

Shuo Yang;Ping Luo;Chen Change Loy;Xiaoou Tang.
computer vision and pattern recognition (2016)

601 Citations

A large-scale car dataset for fine-grained categorization and verification

Linjie Yang;Ping Luo;Chen Change Loy;Xiaoou Tang.
computer vision and pattern recognition (2015)

592 Citations

Compression Artifacts Reduction by a Deep Convolutional Network

Chao Dong;Yubin Deng;Chen Change Loy;Xiaoou Tang.
international conference on computer vision (2015)

550 Citations

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

Xintao Wang;Ke Yu;Shixiang Wu;Jinjin Gu.
european conference on computer vision (2018)

520 Citations

Face alignment by coarse-to-fine shape searching

Shizhan Zhu;Cheng Li;Chen Change Loy;Xiaoou Tang.
computer vision and pattern recognition (2015)

517 Citations

NTIRE 2017 Challenge on Single Image Super-Resolution: Methods and Results

Radu Timofte;Eirikur Agustsson;Luc Van Gool;Ming-Hsuan Yang.
computer vision and pattern recognition (2017)

491 Citations

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Best Scientists Citing Chen Change Loy

Xiaogang Wang

Xiaogang Wang

Chinese University of Hong Kong

Publications: 111

Shaogang Gong

Shaogang Gong

Queen Mary University of London

Publications: 71

Radu Timofte

Radu Timofte

ETH Zurich

Publications: 71

Tao Xiang

Tao Xiang

University of Surrey

Publications: 69

Chunhua Shen

Chunhua Shen

University of Adelaide

Publications: 69

Wanli Ouyang

Wanli Ouyang

University of Sydney

Publications: 69

Liang Lin

Liang Lin

Sun Yat-sen University

Publications: 68

Ming-Hsuan Yang

Ming-Hsuan Yang

University of California, Merced

Publications: 65

Wangmeng Zuo

Wangmeng Zuo

Harbin Institute of Technology

Publications: 62

Timothy M. Hospedales

Timothy M. Hospedales

University of Edinburgh

Publications: 60

Ling Shao

Ling Shao

Inception Institute of Artificial Intelligence

Publications: 59

Luc Van Gool

Luc Van Gool

ETH Zurich

Publications: 57

Jiashi Feng

Jiashi Feng

National University of Singapore

Publications: 55

Dacheng Tao

Dacheng Tao

University of Sydney

Publications: 55

Rama Chellappa

Rama Chellappa

Johns Hopkins University

Publications: 54

Xinbo Gao

Xinbo Gao

Chongqing University of Posts and Telecommunications

Publications: 54

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