World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
58
Citations
15710
World Ranking
3586
National Ranking
1723

Jingkuan Song 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 Jingkuan Song 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: 270 publications — 67th percentile

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

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

Jingkuan Song 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 Jingkuan Song 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: 58 D-Index — 75th percentile

75% of scientists in this discipline score the same or lower.

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

Overview

Jingkuan Song is affiliated with Columbia University in the United States and works primarily in the field of Computer Science. Their research focuses extensively on areas related to Computer Vision and Pattern Recognition, which constitute the majority of their publications. Additional subfields of study include Artificial Intelligence, Signal Processing, Biomedical Engineering, and Computer Networks and Communications.

The main topics of Jingkuan Song's work encompass:

  • Multimodal Machine Learning Applications
  • Advanced Image and Video Retrieval Techniques
  • Domain Adaptation and Few-Shot Learning
  • Human Pose and Action Recognition
  • Anomaly Detection Techniques and Applications
  • Advanced Neural Network Applications
  • Adversarial Robustness in Machine Learning

They have contributed a significant number of papers to several frequent publication venues, including:

  • arXiv (Cornell University)
  • IEEE Transactions on Image Processing
  • Pattern Recognition
  • IEEE Transactions on Multimedia
  • IEEE Transactions on Circuits and Systems for Video Technology

Some of the recent papers authored by or involving Jingkuan Song are:

  • Binary neural networks: A survey (2020), published in Pattern Recognition
  • Explainable deep learning for efficient and robust pattern recognition: A survey of recent developments (2021), published in Pattern Recognition
  • Spatio-Temporal Attention Networks for Action Recognition and Detection (2020), published in IEEE Transactions on Multimedia
  • BATCH: A Scalable Asymmetric Discrete Cross-Modal Hashing (2020), published in IEEE Transactions on Knowledge and Data Engineering
  • Prompting for Multi-Modal Tracking (2022), published in Proceedings of the 30th ACM International Conference on Multimedia

Jingkuan Song has collaborated frequently with a number of coauthors, prominently including Lianli Gao, Heng Tao Shen, Pengpeng Zeng, Xuanhan Wang, and Yuan-Fang Li. These collaborations reflect ongoing partnerships within their research focus areas.

Best Publications

  • A Survey on Learning to Hash

    Jingdong Wang;Ting Zhang;Jingkuan Song;Nicu Sebe

  • Inter-media hashing for large-scale retrieval from heterogeneous data sources

    Jingkuan Song;Yang Yang;Yi Yang;Zi Huang

  • Hashing for Similarity Search: A Survey

    Jingdong Wang;Heng Tao Shen;Jingkuan Song;Jianqiu Ji

  • Learning Deep Representations of Appearance and Motion for Anomalous Event Detection

    Dan Xu;Elisa Ricci;Yan Yan;Jingkuan Song

  • NAIS: Neural Attentive Item Similarity Model for Recommendation

    Xiangnan He;Zhankui He;Jingkuan Song;Zhenguang Liu

  • Binary Neural Networks: A Survey

    Haotong Qin;Ruihao Gong;Xianglong Liu;Xiao Bai

  • Beyond Product Quantization: Deep Progressive Quantization for Image Retrieval

    Lianli Gao;Xiaosu Zhu;Jingkuan Song;Zhou Zhao

  • Multiple feature hashing for real-time large scale near-duplicate video retrieval

    Jingkuan Song;Yi Yang;Zi Huang;Heng Tao Shen

  • Forward and Backward Information Retention for Accurate Binary Neural Networks

    Haotong Qin;Ruihao Gong;Xianglong Liu;Mingzhu Shen

  • Explainable deep learning for efficient and robust pattern recognition: A survey of recent developments

    Xiao Bai;Xiang Wang;Xianglong Liu;Qiang Liu

  • Beyond Frame-level CNN: Saliency-Aware 3-D CNN With LSTM for Video Action Recognition

    Xuanhan Wang;Lianli Gao;Jingkuan Song;Heng Tao Shen

  • Effective Multiple Feature Hashing for Large-Scale Near-Duplicate Video Retrieval

    Jingkuan Song;Yi Yang;Zi Huang;Heng Tao Shen

  • Self-Supervised Video Hashing With Hierarchical Binary Auto-Encoder

    Jingkuan Song;Hanwang Zhang;Xiangpeng Li;Lianli Gao

  • Salience-Guided Cascaded Suppression Network for Person Re-Identification

    Xuesong Chen;Canmiao Fu;Yong Zhao;Feng Zheng

  • From Deterministic to Generative: Multimodal Stochastic RNNs for Video Captioning

    Jingkuan Song;Yuyu Guo;Lianli Gao;Xuelong Li

  • Beyond RNNs: Positional Self-Attention with Co-Attention for Video Question Answering

    Xiangpeng Li;Jingkuan Song;Lianli Gao;Xianglong Liu

  • Quantization-based hashing

    Jingkuan Song;Lianli Gao;Li Liu;Xiaofeng Zhu

  • Hierarchical LSTMs with Adaptive Attention for Visual Captioning

    Lianli Gao;Xiangpeng Li;Jingkuan Song;Heng Tao Shen

  • Ternary Adversarial Networks With Self-Supervision for Zero-Shot Cross-Modal Retrieval

    Xing Xu;Huimin Lu;Jingkuan Song;Yang Yang

  • Local and Global Structure Preservation for Robust Unsupervised Spectral Feature Selection

    Xiaofeng Zhu;Shichao Zhang;Rongyao Hu;Yonghua Zhu

  • Hierarchical LSTMs with Adaptive Attention for Visual Captioning

    Jingkuan Song;Xiangpeng Li;Lianli Gao;Heng Tao Shen

Frequent Co-Authors

Heng Tao Shen
Heng Tao Shen University of Electronic Science and Technology of China
Lianli Gao
Lianli Gao University of Electronic Science and Technology of China
Nicu Sebe
Nicu Sebe University of Trento
Yang Yang
Yang Yang University of Electronic Science and Technology of China
Xianglong Liu
Xianglong Liu Beihang University
Zi Huang
Zi Huang University of Queensland
Fumin Shen
Fumin Shen University of Electronic Science and Technology of China
Yan Yan
Yan Yan Illinois Institute of Technology
Dongxiang Zhang
Dongxiang Zhang Zhejiang University
Wu Liu
Wu Liu University of Science and Technology of China

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