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 30 Citations 3,935 110 World Ranking 8771 National Ranking 845

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Computer vision
  • Machine learning

His primary scientific interests are in Artificial intelligence, Computer vision, Benchmark, Convolutional neural network and Pattern recognition. Many of his studies on Artificial intelligence apply to Deconvolution as well. He has included themes like Geodesic and Synthetic data in his Computer vision study.

Yuchao Dai combines subjects such as RGB color model, Point cloud, Image and Metric with his study of Benchmark. His Convolutional neural network research includes themes of Margin, Translation, Depth map and Color image. The Pattern recognition study combines topics in areas such as Pixel, Ranking, Image sensor and Normal.

His most cited work include:

  • NTIRE 2017 Challenge on Single Image Super-Resolution: Methods and Results (555 citations)
  • Depth and surface normal estimation from monocular images using regression on deep features and hierarchical CRFs (399 citations)
  • A Simple Prior-Free Method for Non-rigid Structure-from-Motion Factorization (211 citations)

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

His scientific interests lie mostly in Artificial intelligence, Computer vision, Pattern recognition, Benchmark and Convolutional neural network. His Artificial intelligence study focuses mostly on Image, Object detection, Depth map, Feature extraction and Feature. His research on Computer vision frequently links to adjacent areas such as Motion.

His study looks at the relationship between Pattern recognition and fields such as RGB color model, as well as how they intersect with chemical problems. His Benchmark research includes elements of Object, Saliency map, Metric and Salience. His Convolutional neural network study combines topics in areas such as Margin, Artificial neural network and Inference.

He most often published in these fields:

  • Artificial intelligence (80.38%)
  • Computer vision (52.53%)
  • Pattern recognition (25.95%)

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

  • Artificial intelligence (80.38%)
  • Benchmark (20.89%)
  • Pattern recognition (25.95%)

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

Yuchao Dai mainly focuses on Artificial intelligence, Benchmark, Pattern recognition, Computer vision and Depth map. In his articles, Yuchao Dai combines various disciplines, including Artificial intelligence and Process. Yuchao Dai has researched Benchmark in several fields, including Metric and Salience.

His Pattern recognition study combines topics in areas such as Object and RGB color model. In general Computer vision, his work in View synthesis, Rendering, 3D reconstruction and Optical flow is often linked to Robotics linking many areas of study. Yuchao Dai combines subjects such as Regularization, Algorithm, Pose and Deep learning with his study of Depth map.

Between 2019 and 2021, his most popular works were:

  • UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders (68 citations)
  • Weakly-Supervised Salient Object Detection via Scribble Annotations (29 citations)
  • Hierarchical Neural Architecture Search for Deep Stereo Matching (16 citations)

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

  • Artificial intelligence
  • Computer vision
  • Machine learning

His primary areas of study are Artificial intelligence, Benchmark, Pattern recognition, Segmentation and Pipeline. He regularly links together related areas like Computer vision in his Artificial intelligence studies. His Computer vision research incorporates elements of Planar, Sequence and Surface.

His Benchmark research includes elements of Probabilistic logic and Inference. The various areas that Yuchao Dai examines in his Pattern recognition study include Latent variable, Object, Object detection, Salience and Saliency map. His Segmentation research incorporates themes from Feature extraction, Filter and Feature.

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

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

Depth and surface normal estimation from monocular images using regression on deep features and hierarchical CRFs

Bo Li;Chunhua Shen;Yuchao Dai;Anton van den Hengel.
computer vision and pattern recognition (2015)

441 Citations

A Simple Prior-Free Method for Non-rigid Structure-from-Motion Factorization

Yuchao Dai;Hongdong Li;Mingyi He.
International Journal of Computer Vision (2014)

255 Citations

Self-Supervised Learning for Stereo Matching with Self-Improving Ability.

Yiran Zhong;Yuchao Dai;Hongdong Li.
arXiv: Computer Vision and Pattern Recognition (2017)

134 Citations

Skeleton based action recognition using translation-scale invariant image mapping and multi-scale deep CNN

Bo Li;Yuchao Dai;Xuelian Cheng;Huahui Chen.
international conference on multimedia and expo (2017)

126 Citations

Deep Stacked Hierarchical Multi-Patch Network for Image Deblurring

Hongguang Zhang;Yuchao Dai;Hongdong Li;Piotr Koniusz.
computer vision and pattern recognition (2019)

116 Citations

UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders

Jing Zhang;Deng-Ping Fan;Yuchao Dai;Saeed Anwar.
computer vision and pattern recognition (2020)

99 Citations

Deep Depth Super-Resolution: Learning Depth Super-Resolution Using Deep Convolutional Neural Network

Xibin Song;Yuchao Dai;Xueying Qin.
asian conference on computer vision (2016)

95 Citations

Monocular depth estimation with hierarchical fusion of dilated CNNs and soft-weighted-sum inference

Bo Li;Yuchao Dai;Mingyi He.
Pattern Recognition (2018)

94 Citations

Efficient Global 2D-3D Matching for Camera Localization in a Large-Scale 3D Map

Liu Liu;Hongdong Li;Yuchao Dai.
international conference on computer vision (2017)

90 Citations

Best Scientists Citing Yuchao Dai

Radu Timofte

Radu Timofte

ETH Zurich

Publications: 64

Luc Van Gool

Luc Van Gool

ETH Zurich

Publications: 50

Hongdong Li

Hongdong Li

Australian National University

Publications: 30

Marc Pollefeys

Marc Pollefeys

ETH Zurich

Publications: 29

Simon Lucey

Simon Lucey

University of Adelaide

Publications: 26

Torsten Sattler

Torsten Sattler

Czech Technical University in Prague

Publications: 25

Peng Wang

Peng Wang

Baidu (China)

Publications: 22

Xinbo Gao

Xinbo Gao

Chongqing University of Posts and Telecommunications

Publications: 21

Wangmeng Zuo

Wangmeng Zuo

Harbin Institute of Technology

Publications: 20

Ruigang Yang

Ruigang Yang

Baidu (China)

Publications: 20

Chunhua Shen

Chunhua Shen

University of Adelaide

Publications: 20

Alan L. Yuille

Alan L. Yuille

Johns Hopkins University

Publications: 19

Fredrik Kahl

Fredrik Kahl

Chalmers University of Technology

Publications: 18

Adrien Bartoli

Adrien Bartoli

University of Clermont Auvergne

Publications: 18

Ming-Ming Cheng

Ming-Ming Cheng

Nankai University

Publications: 18

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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