D-Index & Metrics Best Publications
Xiaohui Shen

Xiaohui Shen

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 55 Citations 14,255 160 World Ranking 2202 National Ranking 214

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, Pattern recognition, Convolutional neural network, Computer vision and Image. His work in Artificial intelligence tackles topics such as Machine learning which are related to areas like Categorization. His biological study spans a wide range of topics, including Object, Boosting, Inference and Image retrieval.

His study in Convolutional neural network is interdisciplinary in nature, drawing from both Segmentation and Image segmentation. His Inpainting study in the realm of Image interacts with subjects such as Process. His Inpainting research is multidisciplinary, incorporating elements of Generative model and Feature.

His most cited work include:

  • A convolutional neural network cascade for face detection (863 citations)
  • Generative Image Inpainting with Contextual Attention (770 citations)
  • A unified approach to salient object detection via low rank matrix recovery (596 citations)

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

Xiaohui Shen spends much of his time researching Artificial intelligence, Computer vision, Pattern recognition, Image and Convolutional neural network. His research on Artificial intelligence often connects related areas such as Machine learning. The study incorporates disciplines such as Salient and Leverage in addition to Computer vision.

His Pattern recognition research includes elements of Object detection and Feature. His Image research integrates issues from Word, Deep learning and Ranking. His biological study deals with issues like Relevance, which deal with fields such as Embedding.

He most often published in these fields:

  • Artificial intelligence (89.67%)
  • Computer vision (43.66%)
  • Pattern recognition (42.72%)

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

  • Artificial intelligence (89.67%)
  • Computer vision (43.66%)
  • Image (31.92%)

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

His primary areas of investigation include Artificial intelligence, Computer vision, Image, Digital image and Artificial neural network. His Artificial intelligence study combines topics from a wide range of disciplines, such as Machine learning and Pattern recognition. The Training set research Xiaohui Shen does as part of his general Pattern recognition study is frequently linked to other disciplines of science, such as Decoupling, therefore creating a link between diverse domains of science.

His Image research incorporates elements of Ranking and Convolutional neural network. His Digital image research includes themes of Depth plane, Joint and Depth of field. His research integrates issues of Ranking and Discriminative model in his study of Deep learning.

Between 2019 and 2021, his most popular works were:

  • Learning progressive joint propagation for human motion prediction (9 citations)
  • EnlightenGAN: Deep Light Enhancement Without Paired Supervision (7 citations)
  • Digital image completion using deep learning (7 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, Computer vision, Human motion, Pixel and Machine learning. He works on Artificial intelligence which deals in particular with Deep learning. His Artificial neural network research extends to Computer vision, which is thematically connected.

His Artificial neural network research is multidisciplinary, relying on both Generative grammar and Discriminative model. His Pixel study frequently draws connections to other fields, such as Matching. The various areas that Xiaohui Shen examines in his Machine learning study include Visualization, Image restoration, Flexibility and Benchmark.

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

A convolutional neural network cascade for face detection

Haoxiang Li;Zhe Lin;Xiaohui Shen;Jonathan Brandt.
computer vision and pattern recognition (2015)

1186 Citations

A unified approach to salient object detection via low rank matrix recovery

Xiaohui Shen;Ying Wu.
computer vision and pattern recognition (2012)

796 Citations

Generative Image Inpainting with Contextual Attention

Jiahui Yu;Zhe Lin;Jimei Yang;Xiaohui Shen.
computer vision and pattern recognition (2018)

774 Citations

Free-Form Image Inpainting With Gated Convolution

Jiahui Yu;Zhe Lin;Jimei Yang;Xiaohui Shen.
international conference on computer vision (2019)

398 Citations

Towards unified depth and semantic prediction from a single image

Peng Wang;Xiaohui Shen;Zhe Lin;Scott Cohen.
computer vision and pattern recognition (2015)

391 Citations

STC: A Simple to Complex Framework for Weakly-Supervised Semantic Segmentation

Yunchao Wei;Xiaodan Liang;Yunpeng Chen;Xiaohui Shen.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2017)

384 Citations

Top-Down Neural Attention by Excitation Backprop

Jianming Zhang;Sarah Adel Bargal;Zhe Lin;Jonathan Brandt.
International Journal of Computer Vision (2018)

347 Citations

Top-Down Neural Attention by Excitation Backprop

Jianming Zhang;Zhe L. Lin;Jonathan Brandt;Xiaohui Shen.
european conference on computer vision (2016)

336 Citations

Minimum Barrier Salient Object Detection at 80 FPS

Jianming Zhang;Stan Sclaroff;Zhe Lin;Xiaohui Shen.
international conference on computer vision (2015)

334 Citations

Semantic Object Parsing with Graph LSTM

Xiaodan Liang;Xiaohui Shen;Jiashi Feng;Liang Lin.
european conference on computer vision (2016)

243 Citations

Best Scientists Citing Xiaohui Shen

Liang Lin

Liang Lin

Sun Yat-sen University

Publications: 58

Huchuan Lu

Huchuan Lu

Dalian University of Technology

Publications: 51

Shuicheng Yan

Shuicheng Yan

National University of Singapore

Publications: 49

Xiaodan Liang

Xiaodan Liang

Sun Yat-sen University

Publications: 48

Ming-Hsuan Yang

Ming-Hsuan Yang

University of California, Merced

Publications: 44

Qi Tian

Qi Tian

Huawei Technologies (China)

Publications: 43

Yunchao Wei

Yunchao Wei

University of Technology Sydney

Publications: 42

Jiashi Feng

Jiashi Feng

National University of Singapore

Publications: 42

Xiaogang Wang

Xiaogang Wang

Chinese University of Hong Kong

Publications: 37

Dacheng Tao

Dacheng Tao

University of Sydney

Publications: 35

Luc Van Gool

Luc Van Gool

ETH Zurich

Publications: 35

Chen Change Loy

Chen Change Loy

Nanyang Technological University

Publications: 30

Chunhua Shen

Chunhua Shen

University of Adelaide

Publications: 29

Liang Zheng

Liang Zheng

Australian National University

Publications: 29

Ming-Ming Cheng

Ming-Ming Cheng

Nankai University

Publications: 29

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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