H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 51 Citations 23,730 111 World Ranking 2796 National Ranking 74

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, Discriminative model, Code and Identity. His study on Image, Contextual image classification and Re identification is often connected to Generalization as part of broader study in Artificial intelligence. His Pattern recognition research is multidisciplinary, incorporating perspectives in Similarity and Minimum bounding box.

His Discriminative model study combines topics from a wide range of disciplines, such as Embedding, Feature learning, Outlier and Euclidean distance. Identity is integrated with Scale and Benchmark in his research. Scale combines with fields such as Ground truth, Scalability, Data mining, Boosting and Visualization in his work.

His most cited work include:

  • Scalable Person Re-identification: A Benchmark (1581 citations)
  • Scalable Person Re-identification: A Benchmark (1581 citations)
  • Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in Vitro (775 citations)

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

The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Discriminative model, Image and Convolutional neural network. The study incorporates disciplines such as Machine learning and Computer vision in addition to Artificial intelligence. Liang Zheng combines subjects such as Embedding, Outlier and Graph with his study of Pattern recognition.

Liang Zheng works mostly in the field of Discriminative model, limiting it down to topics relating to Training set and, in certain cases, Regularization, as a part of the same area of interest. His Convolutional neural network study also includes fields such as

  • Contextual image classification which connect with Object detection,
  • Artificial neural network together with Visualization. The Feature study which covers Benchmark that intersects with Data mining.

He most often published in these fields:

  • Artificial intelligence (84.46%)
  • Pattern recognition (45.95%)
  • Discriminative model (28.38%)

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

  • Artificial intelligence (84.46%)
  • Pattern recognition (45.95%)
  • Machine learning (16.22%)

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

Liang Zheng focuses on Artificial intelligence, Pattern recognition, Machine learning, Image and Feature. As a part of the same scientific study, Liang Zheng usually deals with the Artificial intelligence, concentrating on Computer vision and frequently concerns with Street scene. His Pattern recognition research is multidisciplinary, relying on both RGB color model, Feature, Artificial neural network and Image translation.

The Palette, Color space, Image compression and Color quantization research Liang Zheng does as part of his general Image study is frequently linked to other disciplines of science, such as Color index, therefore creating a link between diverse domains of science. His Feature study also includes

  • Feature extraction that connect with fields like Pose, Iterative reconstruction, Benchmark, Image segmentation and Depth map,
  • Parsing which intersects with area such as Semantics. The Discriminative model study combines topics in areas such as Margin, Ranking and Convolutional neural network.

Between 2019 and 2021, his most popular works were:

  • Random Erasing Data Augmentation (214 citations)
  • Circle Loss: A Unified Perspective of Pair Similarity Optimization (64 citations)
  • Thorax disease classification with attention guided convolutional neural network (27 citations)

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

  • Artificial intelligence
  • Machine learning
  • Computer vision

The scientist’s investigation covers issues in Artificial intelligence, Image, Feature, Feature learning and Feature extraction. His Artificial intelligence study combines topics in areas such as Machine learning and Pattern recognition. His Pattern recognition research incorporates themes from Object detection, Noise and Inference.

His Feature learning research integrates issues from Discrete mathematics, Triplet loss, Decision boundary, Softmax function and Image retrieval. His biological study spans a wide range of topics, including Pose, Outlier, Feature vector and Benchmark. The various areas that Liang Zheng examines in his Convolutional neural network study include Margin and Pooling.

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

Scalable Person Re-identification: A Benchmark

Liang Zheng;Liang Zheng;Liyue Shen;Lu Tian;Shengjin Wang.
international conference on computer vision (2015)

1686 Citations

Re-ranking Person Re-identification with k-Reciprocal Encoding

Zhun Zhong;Liang Zheng;Donglin Cao;Shaozi Li.
computer vision and pattern recognition (2017)

682 Citations

Person Re-identification: Past, Present and Future

Liang Zheng;Yi Yang;Alexander G. Hauptmann.
arXiv: Computer Vision and Pattern Recognition (2016)

673 Citations

Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in Vitro

Zhedong Zheng;Liang Zheng;Yi Yang.
international conference on computer vision (2017)

620 Citations

Random Erasing Data Augmentation

Zhun Zhong;Liang Zheng;Guoliang Kang;Shaozi Li.
arXiv: Computer Vision and Pattern Recognition (2017)

579 Citations

A Discriminatively Learned CNN Embedding for Person Reidentification

Zhedong Zheng;Liang Zheng;Yi Yang.
ACM Transactions on Multimedia Computing, Communications, and Applications (2017)

560 Citations

Beyond Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline)

Yifan Sun;Liang Zheng;Yi Yang;Qi Tian.
european conference on computer vision (2018)

552 Citations

MARS: A Video Benchmark for Large-Scale Person Re-Identification

Liang Zheng;Liang Zheng;Zhi Bie;Yifan Sun;Jingdong Wang.
european conference on computer vision (2016)

527 Citations

SVDNet for Pedestrian Retrieval

Yifan Sun;Liang Zheng;Weijian Deng;Shengjin Wang.
international conference on computer vision (2017)

442 Citations

SIFT Meets CNN: A Decade Survey of Instance Retrieval

Liang Zheng;Yi Yang;Qi Tian.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2018)

342 Citations

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Best Scientists Citing Liang Zheng

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Qi Tian

Huawei Technologies (China)

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Zhejiang University

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Sun Yat-sen University

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Shaogang Gong

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Queen Mary University of London

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Chinese University of Hong Kong

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Ling Shao

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Inception Institute of Artificial Intelligence

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Tao Xiang

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University of Surrey

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Hongsheng Li

Hongsheng Li

Chinese University of Hong Kong

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Xiatian Zhu

Xiatian Zhu

University of Surrey

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Dacheng Tao

Dacheng Tao

University of Sydney

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Wengang Zhou

Wengang Zhou

University of Science and Technology of China

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Jianhuang Lai

Jianhuang Lai

Sun Yat-sen University

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Houqiang Li

Houqiang Li

University of Science and Technology of China

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Lingxi Xie

Lingxi Xie

Huawei Technologies (China)

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Xinbo Gao

Xinbo Gao

Chongqing University of Posts and Telecommunications

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Tao Mei

Tao Mei

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