World's Best Scientists 2026 revealed!
Song Bai

Song Bai

Award Badge
Rising Stars
2025

D-Index & Metrics

Rising Stars

D-Index
53
Citations
12261
World Ranking
244
National Ranking
81

Computer Science

D-Index
53
Citations
14115
World Ranking
4743
National Ranking
635

Song Bai 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 Song Bai 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: 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 publications 991+

This scientist: 107 publications — 11th percentile

11% of scientists in this discipline score the same or lower.

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

Song Bai 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 Song Bai sits on this spectrum.

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 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.

Research.com Recognitions

  • 2025 - Research.com Rising Stars Award

Overview

Song Bai is a researcher affiliated with ByteDance in China, with extensive contributions primarily in the field of Computer Science. Their work centers on subfields such as Computer Vision and Pattern Recognition, Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Electrical and Electronic Engineering, and Materials Chemistry.

The scientist's publication record is notable for frequent contributions to a range of academic venues. These include:

  • arXiv (Cornell University)
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • SSRN Electronic Journal
  • Pattern Recognition

Among recent papers authored or co-authored by Song Bai are:

  • "Hypergraph convolution and hypergraph attention" (2020), published in Pattern Recognition
  • "DanceTrack: Multi-Object Tracking in Uniform Appearance and Diverse Motion" (2022), presented at the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • "End-to-End Temporal Action Detection With Transformer" (2022), published in IEEE Transactions on Image Processing
  • "Learning Transferable Adversarial Examples via Ghost Networks" (2020), published in Proceedings of the AAAI Conference on Artificial Intelligence
  • "Predicting COVID-19 Malignant Progression with AI Techniques" (2020), published in SSRN Electronic Journal

The scholar collaborates frequently with other researchers, including Xiang Bai, Wenqing Zhang, Philip H. S. Torr, Chuhui Xue, and Alan Yuille.

Research topics covered by Song Bai encompass a range of areas within artificial intelligence and computer vision. Key topics include:

  • Advanced Neural Network Applications
  • Advanced Image and Video Retrieval Techniques
  • Human Pose and Action Recognition
  • Multimodal Machine Learning Applications
  • Domain Adaptation and Few-Shot Learning
  • Video Surveillance and Tracking Methods
  • Advanced Vision and Imaging

Best Publications

  • CenterNet: Keypoint Triplets for Object Detection

    Kaiwen Duan;Song Bai;Lingxi Xie;Honggang Qi

  • Improving Transferability of Adversarial Examples With Input Diversity

    Cihang Xie;Zhishuai Zhang;Yuyin Zhou;Song Bai

  • Asymmetric Non-Local Neural Networks for Semantic Segmentation

    Zhen Zhu;Mengdu Xu;Song Bai;Tengteng Huang

  • Hypergraph convolution and hypergraph attention

    Song Bai;Feihu Zhang;Philip H.S. Torr

  • DeepPano: Deep Panoramic Representation for 3-D Shape Recognition

    Baoguang Shi;Song Bai;Zhichao Zhou;Xiang Bai

  • PCL: Proposal Cluster Learning for Weakly Supervised Object Detection

    Peng Tang;Xinggang Wang;Song Bai;Wei Shen

  • Triplet-Center Loss for Multi-view 3D Object Retrieval

    Xinwei He;Yang Zhou;Zhichao Zhou;Song Bai

  • DanceTrack: Multi-Object Tracking in Uniform Appearance and Diverse Motion

    Unknown

  • Scalable Person Re-identification on Supervised Smoothed Manifold

    Song Bai;Xiang Bai;Qi Tian

  • GIFT: A Real-Time and Scalable 3D Shape Search Engine

    Song Bai;Xiang Bai;Zhichao Zhou;Zhaoxiang Zhang

  • End-to-end Temporal Action Detection with Transformer.

    Xiaolong Liu;Qimeng Wang;Yao Hu;Xu Tang

  • Sparse Contextual Activation for Efficient Visual Re-Ranking

    Song Bai;Xiang Bai

  • Prior-Aware Neural Network for Partially-Supervised Multi-Organ Segmentation

    Yuyin Zhou;Zhe Li;Song Bai;Xinlei Chen

  • XingGAN for Person Image Generation

    Hao Tang;Hao Tang;Song Bai;Li Zhang;Philip H. S. Torr

  • Learn to Scale: Generating Multipolar Normalized Density Maps for Crowd Counting

    Chenfeng Xu;Kai Qiu;Jianlong Fu;Song Bai

  • Learning Attraction Field Representation for Robust Line Segment Detection

    Nan Xue;Song Bai;Fudong Wang;Gui-Song Xia

  • Semi-Supervised 3D Abdominal Multi-Organ Segmentation Via Deep Multi-Planar Co-Training

    Yuyin Zhou;Yan Wang;Peng Tang;Song Bai

  • Deep Learning Representation using Autoencoder for 3D Shape Retrieval

    Zhuotun Zhu;Xinggang Wang;Song Bai;Cong Yao

  • SwiftNet: Real-time Video Object Segmentation

    Haochen Wang;Xiaolong Jiang;Haibing Ren;Yao Hu

  • Symmetry-Constrained Rectification Network for Scene Text Recognition

    Mingkun Yang;Yushuo Guan;Minghui Liao;Xin He

  • SHREC'17 Track Large-Scale 3D Shape Retrieval From Shapenet Core55

    Manolis Savva;Fisher Yu;Hao Su;Asako Kanezaki

  • Hard-Aware Point-to-Set Deep Metric for Person Re-identification

    Rui Yu;Zhiyong Dou;Song Bai;Zhaoxiang Zhang

  • Predicting COVID-19 malignant progression with AI techniques

    Cong Fang;Song Bai;Qianlan Chen;Yu Zhou

Frequent Co-Authors

Xiang Bai
Xiang Bai Huazhong University of Science and Technology
Philip H. S. Torr
Philip H. S. Torr University of Oxford
Alan L. Yuille
Alan L. Yuille Johns Hopkins University
Qi Tian
Qi Tian Huawei Technologies (China)
Yuyin Zhou
Yuyin Zhou University of California, Santa Cruz
Longin Jan Latecki
Longin Jan Latecki Temple University
Xinggang Wang
Xinggang Wang Huazhong University of Science and Technology
Nicu Sebe
Nicu Sebe University of Trento
Zhaoxiang Zhang
Zhaoxiang Zhang Chinese Academy of Sciences
Gui-Song Xia
Gui-Song Xia Wuhan University

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