H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 53 Citations 12,172 222 World Ranking 2499 National Ranking 247

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Computer vision

Wei-Shi Zheng spends much of his time researching Artificial intelligence, Pattern recognition, Feature extraction, Computer vision and Machine learning. Much of his study explores Artificial intelligence relationship to Metric. His Pattern recognition research focuses on Robustness and how it relates to Outlier.

His studies in Feature extraction integrate themes in fields like Feature, Curse of dimensionality, Feature selection and Feature vector. His work in Computer vision addresses issues such as Artificial neural network, which are connected to fields such as Computational photography, Contrast, Benchmark and Relation. The various areas that Wei-Shi Zheng examines in his Machine learning study include Data modeling, Visual appearance, Inference and Set.

His most cited work include:

  • Reidentification by Relative Distance Comparison (581 citations)
  • Person Re-Identification by Support Vector Ranking (536 citations)
  • Person re-identification by probabilistic relative distance comparison (522 citations)

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

Artificial intelligence, Pattern recognition, Machine learning, Computer vision and Feature extraction are his primary areas of study. Discriminative model, Image, Feature, Facial recognition system and Re identification are the primary areas of interest in his Artificial intelligence study. His biological study spans a wide range of topics, including Face, Outlier and Robustness.

His study focuses on the intersection of Machine learning and fields such as Metric with connections in the field of Cluster analysis. His work on Histogram, RGB color model, Image processing and Motion as part of general Computer vision research is frequently linked to Invariant, bridging the gap between disciplines. His Feature extraction research includes themes of Deep learning and Feature vector.

He most often published in these fields:

  • Artificial intelligence (86.60%)
  • Pattern recognition (43.79%)
  • Machine learning (28.43%)

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

  • Artificial intelligence (86.60%)
  • Pattern recognition (43.79%)
  • Machine learning (28.43%)

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

Wei-Shi Zheng mostly deals with Artificial intelligence, Pattern recognition, Machine learning, Feature and Discriminative model. His study on Artificial intelligence is mostly dedicated to connecting different topics, such as Computer vision. His Pattern recognition research incorporates elements of Regularization and Abnormality detection.

The Supervised learning and Intelligent decision support system research he does as part of his general Machine learning study is frequently linked to other disciplines of science, such as Event and Focus, therefore creating a link between diverse domains of science. His work carried out in the field of Feature brings together such families of science as Artificial neural network, Embedding, Representation and Pattern recognition. Wei-Shi Zheng has researched Discriminative model in several fields, including Unsupervised learning and Metric.

Between 2019 and 2021, his most popular works were:

  • Unsupervised Person Re-Identification by Deep Asymmetric Metric Embedding (49 citations)
  • Squeeze-and-Attention Networks for Semantic Segmentation (25 citations)
  • Spatial-Temporal Graph Convolutional Network for Video-Based Person Re-Identification (20 citations)

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

  • Artificial intelligence
  • Machine learning
  • Computer vision

His main research concerns Artificial intelligence, Pattern recognition, Feature, Artificial neural network and Discriminative model. His Artificial intelligence study integrates concerns from other disciplines, such as Machine learning and Computer vision. His research integrates issues of Regularization, Spectral clustering and Measure in his study of Pattern recognition.

His Feature research is multidisciplinary, incorporating perspectives in Representation and Pattern recognition. His research in Artificial neural network intersects with topics in Theoretical computer science and Cluster analysis. His study in Discriminative model is interdisciplinary in nature, drawing from both Image and Unsupervised 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

Person Re-Identification by Support Vector Ranking

Bryan James Prosser;Wei-Shi Zheng;Shaogang Gong;Tao Xiang.
british machine vision conference (2010)

761 Citations

Person re-identification by probabilistic relative distance comparison

Wei-Shi Zheng;Shaogang Gong;Tao Xiang.
computer vision and pattern recognition (2011)

714 Citations

Reidentification by Relative Distance Comparison

Wei-Shi Zheng;Shaogang Gong;Tao Xiang.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2013)

706 Citations

Maximum Correntropy Criterion for Robust Face Recognition

Ran He;Wei-Shi Zheng;Bao-Gang Hu.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2011)

600 Citations

Associating Groups of People

Wei-Shi Zheng;Shaogang Gong;Tao Xiang.
british machine vision conference (2009)

446 Citations

Jointly learning heterogeneous features for RGB-D activity recognition

Jian-Fang Hu;Wei-Shi Zheng;Jianhuang Lai;Jianguo Zhang.
computer vision and pattern recognition (2015)

404 Citations

Robust Principal Component Analysis Based on Maximum Correntropy Criterion

Ran He;Bao-Gang Hu;Wei-Shi Zheng;Xiang-Wei Kong.
IEEE Transactions on Image Processing (2011)

285 Citations

Embedding Deep Metric for Person Re-identification: A Study Against Large Variations

Hailin Shi;Yang Yang;Xiangyu Zhu;Shengcai Liao.
european conference on computer vision (2016)

266 Citations

An enhanced deep feature representation for person re-identification

Shangxuan Wu;Ying-Cong Chen;Xiang Li;An-Cong Wu.
workshop on applications of computer vision (2016)

259 Citations

Person Re-Identification by Camera Correlation Aware Feature Augmentation

Ying-Cong Chen;Xiatian Zhu;Wei-Shi Zheng;Jian-Huang Lai.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2018)

242 Citations

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Best Scientists Citing Wei-Shi Zheng

Shaogang Gong

Shaogang Gong

Queen Mary University of London

Publications: 65

Ran He

Ran He

Chinese Academy of Sciences

Publications: 60

Qi Tian

Qi Tian

Huawei Technologies (China)

Publications: 59

Badong Chen

Badong Chen

Xi'an Jiaotong University

Publications: 55

Dacheng Tao

Dacheng Tao

University of Sydney

Publications: 50

Jiwen Lu

Jiwen Lu

Tsinghua University

Publications: 49

Tieniu Tan

Tieniu Tan

Chinese Academy of Sciences

Publications: 45

Xiao-Yuan Jing

Xiao-Yuan Jing

Wuhan University

Publications: 45

Ling Shao

Ling Shao

Inception Institute of Artificial Intelligence

Publications: 43

Jianhuang Lai

Jianhuang Lai

Sun Yat-sen University

Publications: 41

Tao Xiang

Tao Xiang

University of Surrey

Publications: 41

Zhenan Sun

Zhenan Sun

Chinese Academy of Sciences

Publications: 40

Vittorio Murino

Vittorio Murino

Italian Institute of Technology

Publications: 39

Yi Yang

Yi Yang

Zhejiang University

Publications: 37

Xiatian Zhu

Xiatian Zhu

University of Surrey

Publications: 34

Xiaogang Wang

Xiaogang Wang

Chinese University of Hong Kong

Publications: 34

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