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 5,077 113 World Ranking 8788 National Ranking 826

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Computer vision

The scientist’s investigation covers issues in Artificial intelligence, Machine learning, Pattern recognition, Transfer of learning and Algorithm. His research brings together the fields of Computer vision and Artificial intelligence. The Feature research he does as part of his general Machine learning study is frequently linked to other disciplines of science, such as Data modeling, therefore creating a link between diverse domains of science.

His study in Transfer of learning is interdisciplinary in nature, drawing from both Topic model and Semi-supervised learning. His Algorithm study combines topics from a wide range of disciplines, such as RGB color model, Point cloud and FLOPS. His studies deal with areas such as Domain, Projection and Feature vector as well as Embedding.

His most cited work include:

  • Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images (412 citations)
  • Transductive Multi-View Zero-Shot Learning (258 citations)
  • Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks (243 citations)

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

His main research concerns Artificial intelligence, Machine learning, Pattern recognition, Computer vision and Deep learning. His Artificial intelligence research focuses on Image, Feature, Training set, Semantics and Visualization. His studies in Machine learning integrate themes in fields like Class and Benchmark.

His Pattern recognition research includes elements of Transfer of learning, Normalization, Face and Synthetic data. His Computer vision research is multidisciplinary, incorporating perspectives in Process and Representation. His work in Deep learning tackles topics such as Algorithm which are related to areas like Artificial neural network and Point cloud.

He most often published in these fields:

  • Artificial intelligence (79.49%)
  • Machine learning (30.77%)
  • Pattern recognition (19.23%)

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

  • Artificial intelligence (79.49%)
  • Machine learning (30.77%)
  • Computer vision (15.38%)

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

Yanwei Fu spends much of his time researching Artificial intelligence, Machine learning, Computer vision, Feature and Pattern recognition. Yanwei Fu combines Artificial intelligence and Task analysis in his research. His work in the fields of Machine learning, such as Discriminative model and Feature, overlaps with other areas such as Clothing, Matching and Generalization.

The concepts of his Feature study are interwoven with issues in Algorithm, Normalization and Pooling. His research integrates issues of Pascal, Overfitting, Face and Curse of dimensionality in his study of Pattern recognition. Yanwei Fu interconnects Leverage, Embedding, Inference, Categorization and Semantics in the investigation of issues within Training set.

Between 2019 and 2021, his most popular works were:

  • Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers (40 citations)
  • Leader-Based Multi-Scale Attention Deep Architecture for Person Re-Identification (25 citations)
  • Chained-Tracker: Chaining Paired Attentive Regression Results for End-to-End Joint Multiple-Object Detection and Tracking (24 citations)

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

  • Artificial intelligence
  • Machine learning
  • Computer vision

Yanwei Fu mostly deals with Artificial intelligence, Feature extraction, Machine learning, Pattern recognition and Training set. His Artificial intelligence study frequently links to other fields, such as Computer vision. Many of his research projects under Machine learning are closely connected to Matching with Matching, tying the diverse disciplines of science together.

His Pattern recognition study combines topics in areas such as End-to-end principle, Object detection and Source code. His biological study spans a wide range of topics, including Filter, Pruning, Artificial neural network, Pipeline and Inference. His Embedding research is multidisciplinary, relying on both Class, Semantics and Ranking.

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

Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images

Nanyang Wang;Yinda Zhang;Zhuwen Li;Yanwei Fu.
european conference on computer vision (2018)

412 Citations

Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks

Yang He;Yang He;Guoliang Kang;Xuanyi Dong;Yanwei Fu.
international joint conference on artificial intelligence (2018)

359 Citations

Transductive Multi-View Zero-Shot Learning

Yanwei Fu;Timothy M. Hospedales;Tao Xiang;Shaogang Gong.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2015)

277 Citations

Pose-Normalized Image Generation for Person Re-identification

Xuelin Qian;Yanwei Fu;Tao Xiang;Wenxuan Wang.
european conference on computer vision (2018)

223 Citations

Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers

Sixiao Zheng;Jiachen Lu;Hengshuang Zhao;Xiatian Zhu.
computer vision and pattern recognition (2021)

192 Citations

Multi-View Video Summarization

Yanwei Fu;Yanwen Guo;Yanshu Zhu;Feng Liu.
IEEE Transactions on Multimedia (2010)

190 Citations

Transductive Multi-view Embedding for Zero-Shot Recognition and Annotation

Yanwei Fu;Timothy M. Hospedales;Tao Xiang;Zhenyong Fu.
european conference on computer vision (2014)

184 Citations

Learning Multimodal Latent Attributes

Yanwei Fu;Timothy M. Hospedales;Tao Xiang;Shaogang Gong.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2014)

152 Citations

Attribute learning for understanding unstructured social activity

Yanwei Fu;Timothy M. Hospedales;Tao Xiang;Shaogang Gong.
european conference on computer vision (2012)

136 Citations

Multi-scale Deep Learning Architectures for Person Re-identification

Xuelin Qian;Yanwei Fu;Yu-Gang Jiang;Tao Xiang.
international conference on computer vision (2017)

134 Citations

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Best Scientists Citing Yanwei Fu

Tao Xiang

Tao Xiang

University of Surrey

Publications: 54

Ling Shao

Ling Shao

Inception Institute of Artificial Intelligence

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Timothy M. Hospedales

Timothy M. Hospedales

University of Edinburgh

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Shaogang Gong

Shaogang Gong

Queen Mary University of London

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Yi Yang

Yi Yang

Zhejiang University

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Zeynep Akata

Zeynep Akata

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Dacheng Tao

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University of Sydney

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Bernt Schiele

Bernt Schiele

Max Planck Institute for Informatics

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Yanwei Pang

Yanwei Pang

Tianjin University

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Yi-Zhe Song

Yi-Zhe Song

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Wei Liu

Wei Liu

Tencent (China)

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Andreas Geiger

Andreas Geiger

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Qingming Huang

Qingming Huang

Chinese Academy of Sciences

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Kui Jia

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South China University of Technology

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Jungong Han

Jungong Han

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Sanja Fidler

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