World's Best Scientists 2026 revealed!
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Xiaogang Wang
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Computer Science
China
2026

D-Index & Metrics

Discipline name D-Index World Ranking Current World Ranking National Ranking Current National Ranking Publications Citations
Computer Science 153 31 30 1 1 519 121144

Xiaogang Wang publications per year

The chart shows the history of publications by Xiaogang Wang between 1992 and 2025, highlighting the no. of papers published in each year and offering an overview of the publication velocity of this scholar. Xiaogang Wang published across 34 years, from 1992 to 2025, averaging 26.6 papers a year. Output peaked at 110 publications in 2022. 109 of the 904 publications appeared in the last two years.

No. of publications
25 50 75 100
Bar chart. Horizontal axis: year, 1992 to 2025. Vertical axis: number of publications, 0 to 110. Peak 110 publications in 2022. 1992: 1 publication 1993: 0 publications 1994: 1 publication 1995: 0 publications 1996: 0 publications 1997: 0 publications 1998: 0 publications 1999: 0 publications 2000: 0 publications 2001: 2 publications 2002: 5 publications 2003: 7 publications 2004: 15 publications 2005: 6 publications 2006: 9 publications 2007: 15 publications 2008: 17 publications 2009: 17 publications 2010: 15 publications 2011: 23 publications 2012: 28 publications 2013: 43 publications 2014: 54 publications 2015: 40 publications 2016: 50 publications 2017: 59 publications 2018: 61 publications 2019: 67 publications 2020: 40 publications 2021: 25 publications 2022: 110 publications 2023: 85 publications 2024: 71 publications 2025: 38 publications
1992 2025

904 publications in total across all disciplines

View publications per year as a table
Xiaogang Wang: publications per year, 1992 to 2025
Year Publications
1992 1
1993 0
1994 1
1995 0
1996 0
1997 0
1998 0
1999 0
2000 0
2001 2
2002 5
2003 7
2004 15
2005 6
2006 9
2007 15
2008 17
2009 17
2010 15
2011 23
2012 28
2013 43
2014 54
2015 40
2016 50
2017 59
2018 61
2019 67
2020 40
2021 25
2022 110
2023 85
2024 71
2025 38
Total 904
Download as CSV

Xiaogang Wang publications per year - data summary

  • Xiaogang Wang, a Computer Science scholar from Chinese University of Hong Kong, has 904 publications recorded across 34 years, from 1992 to 2025.
  • The oldest publication on record dates to 1992 and the most recent to 2025.
  • The most productive year is 2022, with 110 publications.
  • The least productive years with any output are 1992 and 1994, with 1 publication each.
  • 7 of the 34 years in the span carry no publications at all (1993, 1995, 1996, 1997 and others).
  • The rate of publication averages 26.6 papers per year over the whole span, or 33.5 per year counting only the 27 years with at least one publication.
  • The last 5 years on the chart (2021-2025) hold 329 publications, 36% of the career total.
  • Split into equal eras - 1992-2003: 16 publications (1.3 per year); 2004-2015: 282 publications (23.5 per year); 2016-2025: 606 publications (60.6 per year).
  • Comparing the opening and closing eras, the overall trend of publication is rising.

Xiaogang Wang publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Xiaogang Wang sits on this spectrum.

No. of scientists
200 400 600
Bar chart with 97 bars. Horizontal axis: publications, 32–41 to 991+. Vertical axis: number of scientists, 0 to 609. Most scientists, 609, have 142–151 publications. The last bar groups every scientist with 991 publications or more. The highlighted bar, 512–521 publications, is where this scientist sits. 32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32–41 publications 991+

This scientist: 519 publications — 94th percentile

94% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

View publications distribution as a table
Number of Computer Science scientists by publication count, Research.com 2026 ranking edition. Based on 14,188 ranked scientists.
Publications Scientists This scientist
32–41 7
42–51 22
52–61 82
62–71 134
72–81 249
82–91 324
92–101 421
102–111 420
112–121 497
122–131 544
132–141 555
142–151 609
152–161 559
162–171 534
172–181 556
182–191 583
192–201 519
202–211 508
212–221 490
222–231 437
232–241 423
242–251 408
252–261 377
262–271 301
272–281 335
282–291 320
292–301 293
302–311 250
312–321 238
322–331 206
332–341 209
342–351 208
352–361 162
362–371 176
372–381 127
382–391 158
392–401 128
402–411 104
412–421 94
422–431 99
432–441 83
442–451 108
452–461 73
462–471 77
472–481 69
482–491 84
492–501 62
502–511 54
512–521 57 519
522–531 51
532–541 51
542–551 32
552–561 38
562–571 28
572–581 43
582–591 33
592–601 41
602–611 32
612–621 28
622–631 25
632–641 27
642–651 17
652–661 20
662–671 17
672–681 15
682–691 14
692–701 21
702–711 13
712–721 12
722–731 19
732–741 14
742–751 12
752–761 10
762–771 10
772–781 11
782–791 10
792–801 11
802–811 8
812–821 8
822–831 7
832–841 11
842–851 10
852–861 5
862–871 9
872–881 4
882–891 6
892–901 3
902–911 6
912–921 3
922–931 2
932–941 2
942–951 2
952–961 3
962–971 3
972–981 3
982–990 5
991+ 100
Download as CSV

Xiaogang Wang publication distribution in Computer Science in 2026 - data summary

  • The chart plots the publication count of all 14,188 Computer Science scientists ranked by Research.com in 2026, grouped into 97 ranges running from 32–41 to 991+ publications.
  • Xiaogang Wang, a Computer Science scholar from Chinese University of Hong Kong, records 519 publications - the 94th percentile of the discipline.
  • 94% of ranked Computer Science scientists score the same or lower than Xiaogang Wang, and about 6% score higher.
  • The median of the discipline falls in the 202–211 publications range, and Xiaogang Wang ranks above the median.
  • The most crowded range is 142–151 publications, holding 609 scientists (4% of the field).
  • 70% of the field sits in the lowest quarter of the value range (up to 272–281 publications), so the distribution is heavily right-skewed and high scores are rare.
  • The final bar has no upper bound: it groups every scientist with 991 publications or more, 100 scientists in all (<1% of the field).

Xiaogang Wang D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Xiaogang Wang sits on this spectrum.

No. of scientists
200 400 600 800
Bar chart with 52 bars. Horizontal axis: D-Index, 30–31 to 131+. Vertical axis: number of scientists, 0 to 990. Most scientists, 990, have 36–37 D-Index. The last bar groups every scientist with 131 D-Index or more. The highlighted bar, 131+ D-Index, is where this scientist sits. 30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30–31 D-Index 131+

This scientist: 153 D-Index — 100th percentile

100% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

View D-Index distribution as a table
Number of Computer Science scientists by D-index, Research.com 2026 ranking edition. Based on 14,188 ranked scientists.
D-Index Scientists This scientist
30–31 879
32–33 983
34–35 918
36–37 990
38–39 968
40–41 907
42–43 821
44–45 763
46–47 689
48–49 543
50–51 543
52–53 518
54–55 500
56–57 458
58–59 400
60–61 337
62–63 308
64–65 292
66–67 249
68–69 213
70–71 192
72–73 189
74–75 165
76–77 139
78–79 119
80–81 121
82–83 113
84–85 88
86–87 87
88–89 75
90–91 69
92–93 57
94–95 46
96–97 38
98–99 34
100–101 36
102–103 27
104–105 37
106–107 18
108–109 31
110–111 19
112–113 16
114–115 12
116–117 20
118–119 15
120–121 5
122–123 20
124–125 8
126–127 5
128–129 7
130 3
131+ 98 153
Download as CSV

Xiaogang Wang D-index placement in Computer Science in 2026 - data summary

  • The chart plots the discipline H-index (D-index) of all 14,188 Computer Science scientists ranked by Research.com in 2026, grouped into 52 ranges running from 30–31 to 131+ D-Index.
  • Xiaogang Wang, a Computer Science scholar from Chinese University of Hong Kong, records 153 D-Index - the 100th percentile of the discipline.
  • 100% of ranked Computer Science scientists score the same or lower than Xiaogang Wang, and about 0% score higher.
  • The median of the discipline falls in the 44–45 D-Index range, and Xiaogang Wang ranks above the median.
  • The most crowded range is 36–37 D-Index, holding 990 scientists (7% of the field).
  • 71% of the field sits in the lowest quarter of the value range (up to 54–55 D-Index), so the distribution is heavily right-skewed and high scores are rare.
  • The final bar has no upper bound: it groups every scientist with 131 D-Index or more, 98 scientists in all (<1% of the field).

Research.com Recognitions

  • 2026 - Research.com Computer Science in China Leader Award
  • 2025 - Research.com Computer Science in China Leader Award
  • 2023 - Research.com Computer Science in China Leader Award
  • 2022 - Research.com Computer Science in China Leader Award

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Computer vision

Xiaogang Wang mainly investigates Artificial intelligence, Pattern recognition, Machine learning, Feature extraction and Computer vision. His study in Convolutional neural network, Deep learning, Facial recognition system, Face and Discriminative model falls within the category of Artificial intelligence. The concepts of his Pattern recognition study are interwoven with issues in Feature, Robustness and Benchmark.

His Machine learning research is multidisciplinary, incorporating perspectives in Pose and Training set. His Feature extraction research incorporates elements of Ground truth, Image segmentation, Feature learning and Test set. His work is dedicated to discovering how Computer vision, Pattern recognition are connected with Image-based modeling and rendering and other disciplines.

His most cited work include:

  • Pyramid Scene Parsing Network (3766 citations)
  • Deep Learning Face Attributes in the Wild (3225 citations)
  • DeepReID: Deep Filter Pairing Neural Network for Person Re-identification (1441 citations)

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

Artificial intelligence, Pattern recognition, Computer vision, Machine learning and Convolutional neural network are his primary areas of study. His work is connected to Feature, Object detection, Feature extraction, Artificial neural network and Deep learning, as a part of Artificial intelligence. His Softmax function study in the realm of Deep learning connects with subjects such as Pedestrian detection.

Xiaogang Wang interconnects Contextual image classification, Image and Facial recognition system in the investigation of issues within Pattern recognition. His work carried out in the field of Machine learning brings together such families of science as Representation, Inference and Robustness. His studies deal with areas such as Algorithm, Pose and Conditional random field as well as Convolutional neural network.

He most often published in these fields:

  • Artificial intelligence (90.81%)
  • Pattern recognition (42.17%)
  • Computer vision (33.82%)

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

  • Artificial intelligence (90.81%)
  • Computer vision (33.82%)
  • Pattern recognition (42.17%)

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

His primary areas of study are Artificial intelligence, Computer vision, Pattern recognition, Image and Object detection. His Artificial intelligence research focuses on Machine learning and how it relates to Robustness. His Pattern recognition study deals with Generative grammar intersecting with Image synthesis.

His Object detection study integrates concerns from other disciplines, such as Point cloud, Representation, Minimum bounding box and Transformer. His research integrates issues of Segmentation and Feature extraction in his study of Feature. The study incorporates disciplines such as Image processing, Deep learning, Discriminative model and Leverage in addition to Artificial neural network.

Between 2018 and 2021, his most popular works were:

  • PointRCNN: 3D Object Proposal Generation and Detection From Point Cloud (513 citations)
  • Deep Learning for Generic Object Detection: A Survey (461 citations)
  • StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks (339 citations)

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

  • Artificial intelligence
  • Computer vision
  • Machine learning

His scientific interests lie mostly in Artificial intelligence, Computer vision, Object detection, Pattern recognition and Machine learning. His Artificial intelligence study frequently draws connections between related disciplines such as Natural language processing. His work in Computer vision addresses issues such as Benchmark, which are connected to fields such as Minimum bounding box.

His research on Object detection also deals with topics like

  • Artificial neural network and Voxel most often made with reference to Point cloud,
  • End-to-end principle that connect with fields like Image resolution. He has included themes like Structure, Generative grammar and Feature in his Pattern recognition study. His research in Machine learning intersects with topics in Tree traversal and Robustness.

Best Publications

  • Pyramid Scene Parsing Network

    Hengshuang Zhao;Jianping Shi;Xiaojuan Qi;Xiaogang Wang

  • Deep Learning Face Attributes in the Wild

    Ziwei Liu;Ping Luo;Xiaogang Wang;Xiaoou Tang

  • Residual Attention Network for Image Classification

    Fei Wang;Mengqing Jiang;Chen Qian;Shuo Yang

  • DeepReID: Deep Filter Pairing Neural Network for Person Re-identification

    Wei Li;Rui Zhao;Tong Xiao;Xiaogang Wang

  • Deep Learning for Generic Object Detection: A Survey

    Li Liu;Li Liu;Wanli Ouyang;Xiaogang Wang;Paul W. Fieguth

  • PointRCNN: 3D Object Proposal Generation and Detection From Point Cloud

    Shaoshuai Shi;Xiaogang Wang;Hongsheng Li

  • StackGAN: Text to Photo-Realistic Image Synthesis with Stacked Generative Adversarial Networks

    Han Zhang;Tao Xu;Hongsheng Li

  • Deep Learning Face Representation from Predicting 10,000 Classes

    Yi Sun;Xiaogang Wang;Xiaoou Tang

  • Deep Learning Face Representation by Joint Identification-Verification

    Yi Sun;Yuheng Chen;Xiaogang Wang;Xiaoou Tang

  • PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection

    Shaoshuai Shi;Chaoxu Guo;Li Jiang;Zhe Wang

  • DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations

    Ziwei Liu;Ping Luo;Shi Qiu;Xiaogang Wang

  • Deep Convolutional Network Cascade for Facial Point Detection

    Yi Sun;Xiaogang Wang;Xiaoou Tang

  • StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks

    Han Zhang;Tao Xu;Hongsheng Li;Shaoting Zhang

  • Context Encoding for Semantic Segmentation

    Hang Zhang;Kristin Dana;Jianping Shi;Zhongyue Zhang

  • Unsupervised Salience Learning for Person Re-identification

    Rui Zhao;Wanli Ouyang;Xiaogang Wang

  • Cross-scene crowd counting via deep convolutional neural networks

    Cong Zhang;Hongsheng Li;Xiaogang Wang;Xiaokang Yang

  • Visual Tracking with Fully Convolutional Networks

    Lijun Wang;Wanli Ouyang;Xiaogang Wang;Huchuan Lu

  • StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks

    Han Zhang;Tao Xu;Hongsheng Li;Shaoting Zhang

  • DeepID3: Face Recognition with Very Deep Neural Networks

    Yi Sun;Ding Liang;Xiaogang Wang;Xiaoou Tang

  • Saliency detection by multi-context deep learning

    Rui Zhao;Wanli Ouyang;Hongsheng Li;Xiaogang Wang

  • Learning from massive noisy labeled data for image classification

    Tong Xiao;Tian Xia;Yi Yang;Chang Huang

Frequent Co-Authors

Hongsheng Li
Hongsheng Li Chinese University of Hong Kong
Xiaoou Tang
Xiaoou Tang Chinese University of Hong Kong
Wanli Ouyang
Wanli Ouyang Shanghai AI Lab
Ping Luo
Ping Luo University of Hong Kong
Shuai Yi
Shuai Yi SenseTime
Junjie Yan
Junjie Yan SenseTime
Bolei Zhou
Bolei Zhou University of California, Los Angeles
Jianping Shi
Jianping Shi SenseTime
Ziwei Liu
Ziwei Liu Nanyang Technological University
Chen Change Loy
Chen Change Loy Nanyang Technological University

External Links

Personal website of Xiaogang Wang Google Scholar page

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Publications: 137

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Publications: 132

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Publications: 121

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Publications: 121

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National University of Singapore

Publications: 118

Shaogang Gong

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Queen Mary University of London

Publications: 113

Stan Z. Li

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Westlake University

Publications: 110

Liang Lin

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Sun Yat-sen University

Publications: 110

Huchuan Lu

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Publications: 106

Zhen Lei

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Chinese Academy of Sciences

Publications: 105

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Publications: 104

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Publications: 102

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Publications: 101

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Publications: 100

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2027 Communication Disorders Clinical Hours Requirements: What Counts and How to Complete Them
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2027 Licensing Exams Required After Completing a Science Degree Program