World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
68
Citations
35083
World Ranking
2023
National Ranking
1023

Marcus Rohrbach 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 Marcus Rohrbach 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: 104 publications — 10th percentile

10% of scientists in this discipline score the same or lower.

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

Marcus Rohrbach 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 Marcus Rohrbach 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: 68 D-Index — 86th percentile

86% of scientists in this discipline score the same or lower.

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

Overview

Marcus Rohrbach is a researcher primarily affiliated with Facebook in the United States. Their work is concentrated in the field of Computer Science, with a strong focus on Computer Vision and Pattern Recognition, Artificial Intelligence, and related subfields.

Their recent scholarly contributions include publications in several notable venues:

  • FLAVA: A Foundational Language And Vision Alignment Model, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • Learning To Recognize Procedural Activities with Distant Supervision, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • FLAVA: A Foundational Language And Vision Alignment Model, 2021, arXiv (Cornell University)
  • TextCaps: A Dataset for Image Captioning with Reading Comprehension, 2020, Lecture notes in computer science
  • In Defense of Grid Features for Visual Question Answering, 2020, arXiv (Cornell University)

Marcus Rohrbach's research output has a notable presence in publication venues such as:

  • arXiv (Cornell University)
  • Lecture notes in computer science
  • 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Applied AI Letters

Their work spans several research topics including:

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

Frequent collaborators in their research include Laura Sevilla-Lara, Frank Keller, Shreyank N Gowda, Trevor Darrell, and Suzanne Petryk.

Marcus Rohrbach's body of work demonstrates sustained contributions to advancing methodologies and applications within Computer Vision and Artificial Intelligence. Their research on foundational alignment models for language and vision, procedural activity recognition, and datasets for image captioning reflects a broad engagement with current challenges in the field.

Best Publications

  • Long-term recurrent convolutional networks for visual recognition and description

    Jeff Donahue;Lisa Anne Hendricks;Sergio Guadarrama;Marcus Rohrbach

  • Long-Term Recurrent Convolutional Networks for Visual Recognition and Description

    Jeff Donahue;Lisa Anne Hendricks;Marcus Rohrbach;Subhashini Venugopalan

  • Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding

    Akira Fukui;Dong Huk Park;Daylen Yang;Anna Rohrbach

  • Sequence to Sequence -- Video to Text

    Subhashini Venugopalan;Marcus Rohrbach;Jeffrey Donahue;Raymond Mooney

  • Memory Aware Synapses: Learning What (not) to Forget

    Rahaf Aljundi;Francesca Babiloni;Mohamed Elhoseiny;Marcus Rohrbach

  • Neural Module Networks

    Jacob Andreas;Marcus Rohrbach;Trevor Darrell;Dan Klein

  • Translating Videos to Natural Language Using Deep Recurrent Neural Networks

    Subhashini Venugopalan;Huijuan Xu;Jeff Donahue;Marcus Rohrbach

  • FLAVA: A Foundational Language And Vision Alignment Model

    Unknown

  • Ask Your Neurons: A Neural-Based Approach to Answering Questions about Images

    Mateusz Malinowski;Marcus Rohrbach;Mario Fritz

  • Efficient Lifelong Learning with A-GEM

    Arslan Chaudhry;Marc'Aurelio Ranzato;Marcus Rohrbach;Mohamed Elhoseiny

  • A database for fine grained activity detection of cooking activities

    Marcus Rohrbach;Sikandar Amin;Mykhaylo Andriluka;Bernt Schiele

  • Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks With Octave Convolution

    Yunpeng Chen;Haoqi Fan;Bing Xu;Zhicheng Yan

  • Generating Visual Explanations

    Lisa Anne Hendricks;Zeynep Akata;Marcus Rohrbach;Marcus Rohrbach;Jeff Donahue

  • Grounding of Textual Phrases in Images by Reconstruction

    Anna Rohrbach;Marcus Rohrbach;Marcus Rohrbach;Ronghang Hu;Trevor Darrell

  • Learning to Reason: End-to-End Module Networks for Visual Question Answering

    Ronghang Hu;Jacob Andreas;Marcus Rohrbach;Trevor Darrell

  • Natural Language Object Retrieval

    Ronghang Hu;Huazhe Xu;Marcus Rohrbach;Jiashi Feng

  • Decoupling Representation and Classifier for Long-Tailed Recognition

    Bingyi Kang;Saining Xie;Marcus Rohrbach;Zhicheng Yan

  • Learning to Compose Neural Networks for Question Answering

    Jacob Andreas;Marcus Rohrbach;Trevor Darrell;Dan Klein

  • A dataset for Movie Description

    Anna Rohrbach;Marcus Rohrbach;Niket Tandon;Bernt Schiele

  • Graph-Based Global Reasoning Networks

    Yunpeng Chen;Marcus Rohrbach;Zhicheng Yan;Yan Shuicheng

  • On Tiny Episodic Memories in Continual Learning

    Arslan Chaudhry;Marcus Rohrbach;Mohamed Elhoseiny;Thalaiyasingam Ajanthan

Frequent Co-Authors

Trevor Darrell
Trevor Darrell University of California, Berkeley
Bernt Schiele
Bernt Schiele Max Planck Institute for Informatics
Anna Rohrbach
Anna Rohrbach Technical University of Darmstadt
Kate Saenko
Kate Saenko Boston University
Devi Parikh
Devi Parikh Facebook (United States)
Xinlei Chen
Xinlei Chen Facebook (United States)
Dhruv Batra
Dhruv Batra Georgia Institute of Technology
Jeff Donahue
Jeff Donahue DeepMind (United Kingdom)
Raymond J. Mooney
Raymond J. Mooney The University of Texas at Austin

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