H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 51 Citations 11,638 180 World Ranking 2708 National Ranking 257

Research.com Recognitions

Awards & Achievements

2019 - IEEE Fellow For contributions to geometric and image-based modeling

2018 - ACM Distinguished Member

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Computer vision
  • Machine learning

The scientist’s investigation covers issues in Artificial intelligence, Computer vision, Pattern recognition, Convolutional neural network and Salience. His study in Feature extraction, Artificial neural network, Image, Object and Deep learning are all subfields of Artificial intelligence. His Feature extraction research includes themes of Kadir–Brady saliency detector and Representation.

The concepts of his Pattern recognition study are interwoven with issues in Contextual image classification and Feature. The Convolutional neural network study combines topics in areas such as RGB color model and Image. In his study, Salient and Segmentation-based object categorization is strongly linked to Object detection, which falls under the umbrella field of Image segmentation.

His most cited work include:

  • Visual saliency based on multiscale deep features (475 citations)
  • Mesh editing with poisson-based gradient field manipulation (473 citations)
  • Efficient View-Dependent Image-Based Rendering with Projective Texture-Mapping (461 citations)

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

His primary scientific interests are in Artificial intelligence, Computer vision, Pattern recognition, Convolutional neural network and Image. His Artificial intelligence study frequently links to related topics such as Machine learning. As a part of the same scientific family, Yizhou Yu mostly works in the field of Computer vision, focusing on Computer graphics and, on occasion, Algorithm.

His study in Pattern recognition is interdisciplinary in nature, drawing from both Artificial neural network, Object detection and Contextual image classification. His Convolutional neural network study combines topics from a wide range of disciplines, such as Benchmark and Salience. His research integrates issues of Salient and Saliency map in his study of Salience.

He most often published in these fields:

  • Artificial intelligence (75.37%)
  • Computer vision (33.46%)
  • Pattern recognition (31.25%)

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

  • Artificial intelligence (75.37%)
  • Pattern recognition (31.25%)
  • Convolutional neural network (19.12%)

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

Yizhou Yu focuses on Artificial intelligence, Pattern recognition, Convolutional neural network, Segmentation and Image. He interconnects Computer vision and Natural language processing in the investigation of issues within Artificial intelligence. In general Pattern recognition study, his work on Discriminative model and Image segmentation often relates to the realm of Domain and Consistency, thereby connecting several areas of interest.

His research on Convolutional neural network also deals with topics like

  • Object detection and related Feature extraction,
  • Robustness together with Pyramid, Artificial neural network and Ground truth. His Segmentation research incorporates elements of Contextual image classification, Dynamic programming and Stroke. The various areas that he examines in his Image study include Smoothing, Generator and Data mining.

Between 2018 and 2021, his most popular works were:

  • Neural Style Transfer: A Review (156 citations)
  • FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation. (63 citations)
  • Weakly Supervised Complementary Parts Models for Fine-Grained Image Classification From the Bottom Up (60 citations)

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

  • Artificial intelligence
  • Computer vision
  • Machine learning

His main research concerns Artificial intelligence, Pattern recognition, Convolutional neural network, Feature and Computer vision. Artificial intelligence is closely attributed to Natural language processing in his work. His work on Segmentation and Transduction is typically connected to Class and Domain as part of general Pattern recognition study, connecting several disciplines of science.

Within one scientific family, Yizhou Yu focuses on topics pertaining to Robustness under Convolutional neural network, and may sometimes address concerns connected to Pyramid, Adversarial system, Image segmentation and Artificial neural network. His Pyramid study, which is part of a larger body of work in Feature, is frequently linked to Volume, bridging the gap between disciplines. His Computer vision study integrates concerns from other disciplines, such as X ray diagnosis and Identification.

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

Efficient View-Dependent Image-Based Rendering with Projective Texture-Mapping

Paul Debevec;Yizhou Yu;George Boshokov.
eurographics (1998)

780 Citations

Mesh editing with poisson-based gradient field manipulation

Yizhou Yu;Kun Zhou;Dong Xu;Xiaohan Shi.
international conference on computer graphics and interactive techniques (2004)

718 Citations

Inverse global illumination: recovering reflectance models of real scenes from photographs

Yizhou Yu;Paul Debevec;Jitendra Malik;Tim Hawkins.
international conference on computer graphics and interactive techniques (1999)

605 Citations

Visual saliency based on multiscale deep features

Guanbin Li;Yizhou Yu.
computer vision and pattern recognition (2015)

509 Citations

Deep Contrast Learning for Salient Object Detection

Guanbin Li;Yizhou Yu.
computer vision and pattern recognition (2016)

468 Citations

HD-CNN: Hierarchical Deep Convolutional Neural Networks for Large Scale Visual Recognition

Zhicheng Yan;Hao Zhang;Robinson Piramuthu;Vignesh Jagadeesh.
international conference on computer vision (2015)

349 Citations

Feature matching and deformation for texture synthesis

Qing Wu;Yizhou Yu.
international conference on computer graphics and interactive techniques (2004)

293 Citations

Particle-based simulation of granular materials

Nathan Bell;Yizhou Yu;Peter J. Mucha.
symposium on computer animation (2005)

286 Citations

Recovering photometric properties of architectural scenes from photographs

Yizhou Yu;Jitendra Malik.
international conference on computer graphics and interactive techniques (1998)

272 Citations

Visual Saliency Detection Based on Multiscale Deep CNN Features

Guanbin Li;Yizhou Yu.
IEEE Transactions on Image Processing (2016)

258 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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