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D-Index & Metrics

Computer Science

D-Index
43
Citations
26827
World Ranking
7733
National Ranking
3338

Philipp Krähenbühl 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 Philipp Krähenbühl 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: 54 publications — 1st percentile

1% of scientists in this discipline score the same or lower.

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

Philipp Krähenbühl 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 Philipp Krähenbühl 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: 43 D-Index — 46th percentile

46% of scientists in this discipline score the same or lower.

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

Overview

Philipp Krähenbühl is affiliated with The University of Texas at Austin in the United States. Their research work primarily belongs to the field of Computer Science, with a strong focus on Computer Vision and Pattern Recognition.

Their research spans various subfields, including Artificial Intelligence, Molecular Biology, Aerospace Engineering, and Automotive Engineering. This variety reflects interdisciplinary interests alongside a core concentration on visual computing and machine learning techniques.

Krähenbühl has contributed to topics such as:

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

The scientist's recent notable publications include:

  • Cross-view Transformers for real-time Map-view Semantic Segmentation, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • Global Tracking Transformers, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • Probabilistic two-stage detection, 2021, arXiv (Cornell University)
  • Multimodal Virtual Point 3D Detection, 2021, arXiv (Cornell University)
  • Simple Multi-dataset Detection, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Krähenbühl frequently publishes in venues such as:

  • arXiv (Cornell University)
  • 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • bioRxiv (Cold Spring Harbor Laboratory)
  • 2021 IEEE/CVF International Conference on Computer Vision (ICCV)

Frequently collaborating researchers with Krähenbühl include:

  • Xingyi Zhou
  • Vladlen Koltun
  • Jang Hyun Cho
  • Brady Zhou
  • Tianwei Yin

Their work involves a mix of advanced methodologies for both theoretical and applied problems within computer vision, machine learning, and related areas.

Best Publications

  • Context Encoders: Feature Learning by Inpainting

    Deepak Pathak;Philipp Krahenbuhl;Jeff Donahue;Trevor Darrell

  • Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials

    Philipp Krähenbühl;Vladlen Koltun

  • Saliency filters: Contrast based filtering for salient region detection

    Federico Perazzi;Philipp Krahenbuhl;Yael Pritch;Alexander Hornung

  • Center-based 3D Object Detection and Tracking

    Tianwei Yin;Xingyi Zhou;Philipp Krahenbuhl

  • Generative Visual Manipulation on the Natural Image Manifold

    Jun-Yan Zhu;Philipp Krähenbühl;Eli Shechtman;Alexei A. Efros

  • Objects as Points

    Xingyi Zhou;Dequan Wang;Philipp Krähenbühl

  • Tracking Objects as Points

    Xingyi Zhou;Vladlen Koltun;Philipp Krähenbühl

  • Adversarial Feature Learning

    Jeff Donahue;Philipp Krähenbühl;Trevor Darrell

  • Bottom-Up Object Detection by Grouping Extreme and Center Points

    Xingyi Zhou;Jiacheng Zhuo;Philipp Krahenbuhl

  • Sampling Matters in Deep Embedding Learning

    R. Manmatha;Chao-Yuan Wu;Alexander J. Smola;Philipp Krahenbuhl

  • Constrained Convolutional Neural Networks for Weakly Supervised Segmentation

    Deepak Pathak;Philipp Krahenbuhl;Trevor Darrell

  • Long-Term Feature Banks for Detailed Video Understanding

    Chao-Yuan Wu;Christoph Feichtenhofer;Haoqi Fan;Kaiming He

  • Geodesic Object Proposals

    Philipp Krähenbühl;Vladlen Koltun

  • Learning Dense Correspondence via 3D-Guided Cycle Consistency

    Tinghui Zhou;Philipp Krahenbuhl;Mathieu Aubry;Qixing Huang

  • Compressed Video Action Recognition

    Chao-Yuan Wu;Manzil Zaheer;Hexiang Hu;R. Manmatha

  • Video Compression Through Image Interpolation

    Chao-Yuan Wu;Nayan Singhal;Philipp Krähenbühl

  • A system for retargeting of streaming video

    Philipp Krähenbühl;Manuel Lang;Alexander Hornung;Markus Gross

  • Global Tracking Transformers

    Unknown

  • Data-dependent Initializations of Convolutional Neural Networks

    Philipp Krähenbühl;Carl Doersch;Carl Doersch;Jeff Donahue;Trevor Darrell

  • Gesture controllers

    Sergey Levine;Philipp Krähenbühl;Sebastian Thrun;Vladlen Koltun

  • Assessing Generalization in Deep Reinforcement Learning

    Charles Packer;Katelyn Gao;Jernej Kos;Philipp Krähenbühl

  • Joint Monocular 3D Vehicle Detection and Tracking.

    Hou-Ning Hu;Qi-Zhi Cai;Dequan Wang;Ji Lin

Frequent Co-Authors

Vladlen Koltun
Vladlen Koltun Apple (United States)
Trevor Darrell
Trevor Darrell University of California, Berkeley
Alexei A. Efros
Alexei A. Efros University of California, Berkeley
Jeff Donahue
Jeff Donahue DeepMind (United Kingdom)
Kaiming He
Kaiming He Facebook (United States)
R. Manmatha
R. Manmatha Amazon (United States)
Alexander J. Smola
Alexander J. Smola Amazon (United States)
Eli Shechtman
Eli Shechtman Adobe Systems (United States)
Christoph Feichtenhofer
Christoph Feichtenhofer Meta Platforms, Inc.
Jun-Yan Zhu
Jun-Yan Zhu Carnegie Mellon University

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