World's Best Scientists 2026 revealed!
Jingjing Liu

Jingjing Liu

D-Index & Metrics

Computer Science

D-Index
53
Citations
12792
World Ranking
4772
National Ranking
2220

Jingjing Liu 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 Jingjing Liu sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 250 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: 560 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: 424 scientists 242–251 publications: 408 scientists 252–261 publications: 378 scientists 262–271 publications: 300 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 publications 991+

This scientist: 128 publications — 18th percentile

18% of scientists in this discipline score the same or lower.

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

Jingjing Liu 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 Jingjing Liu sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 984 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 969 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 765 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 517 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 D-Index 131+

This scientist: 53 D-Index — 67th percentile

67% of scientists in this discipline score the same or lower.

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

Overview

Jingjing Liu is affiliated with MIT in the United States and has a significant body of research primarily in the field of Computer Science. Their work spans multiple subfields, including Computer Vision and Pattern Recognition, Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Media Technology, and Computational Mechanics.

The main topics explored in their research include:

  • Multimodal Machine Learning Applications
  • Domain Adaptation and Few-Shot Learning
  • Advanced Neural Network Applications
  • Topic Modeling
  • Advanced Image and Video Retrieval Techniques
  • Image Enhancement Techniques
  • Natural Language Processing Techniques

Jingjing Liu's recent papers reflect a focus on vision and language representation learning, domain adaptation, and image detection techniques. Notable papers include:

  • "Large-Scale Adversarial Training for Vision-and-Language Representation Learning" (2020), published in arXiv (Cornell University)
  • "TVT: Transferable Vision Transformer for Unsupervised Domain Adaptation" (2023), presented at the 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
  • "Insulator Faults Detection in Aerial Images from High-Voltage Transmission Lines Based on Deep Learning Model" (2021), published in Applied Sciences
  • "Improved YOLOv3 Network for Insulator Detection in Aerial Images with Diverse Background Interference" (2021), published in Electronics
  • "Target Detection of Forward-Looking Sonar Image Based on Improved YOLOv5" (2022), published in IEEE Access

Frequent coauthors in Jingjing Liu's collaborations include:

  • Zhe Gan
  • Linjie Li
  • Shuohang Wang
  • Yen-Chun Chen
  • Yu Cheng

Jingjing Liu has published extensively in several key venues, with numerous contributions to:

  • arXiv (Cornell University)
  • IEEE Access
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Energies
  • Neurocomputing

Their work is predominantly situated within advanced machine learning applications, integrating techniques in both computer vision and language modeling, underscoring an interdisciplinary approach across imaging and artificial intelligence domains.

Best Publications

  • UNITER: UNiversal Image-TExt Representation Learning

    Yen-Chun Chen;Linjie Li;Licheng Yu;Ahmed El Kholy

  • DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation

    Yizhe Zhang;Siqi Sun;Michel Galley;Yen-Chun Chen

  • Patient Knowledge Distillation for BERT Model Compression

    Siqi Sun;Yu Cheng;Zhe Gan;Jingjing Liu

  • Less is More: CLIPBERT for Video-and-Language Learning via Sparse Sampling

    Jie Lei;Linjie Li;Luowei Zhou;Zhe Gan

  • Low-Quality Product Review Detection in Opinion Summarization

    Jingjing Liu;Yunbo Cao;Chin-Yew Lin;Yalou Huang

  • Multispectral Deep Neural Networks for Pedestrian Detection

    Jingjing Liu;Shaoting Zhang;Shu Wang;Dimitris N. Metaxas

  • HERO: Hierarchical Encoder for Video+Language Omni-representation Pre-training

    Linjie Li;Yen-Chun Chen;Yu Cheng;Zhe Gan

  • Relation-Aware Graph Attention Network for Visual Question Answering

    Linjie Li;Zhe Gan;Yu Cheng;Jingjing Liu

  • FreeLB: Enhanced Adversarial Training for Natural Language Understanding

    Chen Zhu;Yu Cheng;Zhe Gan;Siqi Sun

  • UNITER: Learning UNiversal Image-TExt Representations

    Yen-Chun Chen;Linjie Li;Licheng Yu;Ahmed El Kholy

  • Large-Scale Adversarial Training for Vision-and-Language Representation Learning

    Zhe Gan;Yen-Chun Chen;Linjie Li;Chen Zhu

  • ReCoRD: Bridging the Gap between Human and Machine Commonsense Reading Comprehension.

    Sheng Zhang;Xiaodong Liu;Jingjing Liu;Jianfeng Gao

  • Discourse-Aware Neural Extractive Text Summarization

    Jiacheng Xu;Zhe Gan;Yu Cheng;Jingjing Liu

  • StoryGAN: A Sequential Conditional GAN for Story Visualization

    Yitong Li;Zhe Gan;Yelong Shen;Jingjing Liu

  • Deep Dyna-Q: Integrating Planning for Task-Completion Dialogue Policy Learning

    Baolin Peng;Xiujun Li;Jianfeng Gao;Jingjing Liu

  • Video search re-ranking via multi-graph propagation

    Jingjing Liu;Wei Lai;Xian-Sheng Hua;Yalou Huang

  • Hierarchical Graph Network for Multi-hop Question Answering

    Yuwei Fang;Siqi Sun;Zhe Gan;Rohit Pillai

  • TVT: Transferable Vision Transformer for Unsupervised Domain Adaptation

    Unknown

  • Tactical Rewind: Self-Correction via Backtracking in Vision-And-Language Navigation

    Liyiming Ke;Xiujun Li;Yonatan Bisk;Ari Holtzman

  • Review Sentiment Scoring via a Parse-and-Paraphrase Paradigm

    Jingjing Liu;Stephanie Seneff

  • Integrating planning for task-completion dialogue policy learning.

    Baolin Peng;Xiujun Li;Jianfeng Gao;Jingjing Liu

Frequent Co-Authors

Zhe Gan
Zhe Gan Microsoft (United States)
Yu Cheng
Yu Cheng Microsoft (United States)
Jianfeng Gao
Jianfeng Gao Microsoft (United States)
Dimitris N. Metaxas
Dimitris N. Metaxas Rutgers, The State University of New Jersey
Lawrence Carin
Lawrence Carin Duke University
Zhangyang Wang
Zhangyang Wang The University of Texas at Austin
Shaoting Zhang
Shaoting Zhang University of Electronic Science and Technology of China
Tom Goldstein
Tom Goldstein University of Maryland, College Park
Jing Jiang
Jing Jiang Singapore Management University

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