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Computer Science
UK
2026

D-Index & Metrics

Computer Science

D-Index
134
Citations
97907
World Ranking
88
National Ranking
4

Philip H. S. Torr 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 Philip H. S. Torr 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: 583 publications — 96th percentile

96% of scientists in this discipline score the same or lower.

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

Philip H. S. Torr 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 Philip H. S. Torr 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: 134 D-Index — 99th percentile

99% 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

  • 2026 - Research.com Computer Science in United Kingdom Leader Award
  • 2025 - Research.com Computer Science in United Kingdom Leader Award
  • 2023 - Research.com Computer Science in United Kingdom Leader Award
  • 2022 - Research.com Computer Science in United Kingdom Leader Award
  • 2019 - Fellow of the Royal Academy of Engineering (UK)
  • 2012 - Fellow of the International Association for Pattern Recognition (IAPR) For contributions to robust computer vision

Overview

Philip H. S. Torr is a researcher affiliated with the University of Oxford in the United Kingdom. Their scientific contributions primarily fall within the field of Computer Science, with a substantial focus on Computer Vision and Pattern Recognition as well as Artificial Intelligence.

Their body of work encompasses several key topics, including:

  • Domain Adaptation and Few-Shot Learning
  • Advanced Neural Network Applications
  • Multimodal Machine Learning Applications
  • Adversarial Robustness in Machine Learning
  • Advanced Image and Video Retrieval Techniques
  • Generative Adversarial Networks and Image Synthesis
  • Machine Learning and Data Classification

Significant recent publications authored or co-authored by Philip H. S. Torr include:

  • "Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?", 2020, arXiv (Cornell University)
  • "FACMAC: Factored Multi-Agent Centralised Policy Gradients", 2020, arXiv (Cornell University)
  • "SiamMask: A Framework for Fast Online Object Tracking and Segmentation.", 2023, PubMed
  • "Solving Inefficiency of Self-supervised Representation Learning", 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • "Open World Entity Segmentation", 2022, IEEE Transactions on Pattern Analysis and Machine Intelligence

Philip H. S. Torr's collaborations are considerable, with frequent coauthors including:

  • Adel Bibi
  • Bernard Ghanem
  • Puneet K. Dokania
  • Motasem Alfarra
  • Pau de Jorge

Their research outputs have been published extensively in various venues, notably:

  • arXiv (Cornell University)
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • PubMed
  • 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence

Philip H. S. Torr has been recognized with several awards, including appointment as a Fellow of the Royal Academy of Engineering (UK) in 2019. Additionally, they were named Fellow of the International Association for Pattern Recognition (IAPR) in 2012 for contributions to robust computer vision.

Best Publications

  • Learning to Compare: Relation Network for Few-Shot Learning

    Flood Sung;Yongxin Yang;Li Zhang;Tao Xiang

  • Fully-Convolutional Siamese Networks for Object Tracking

    Luca Bertinetto;Jack Valmadre;João F. Henriques;Andrea Vedaldi

  • Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers

    Sixiao Zheng;Jiachen Lu;Hengshuang Zhao;Xiatian Zhu

  • Struck: Structured Output Tracking with Kernels

    Sam Hare;Stuart Golodetz;Amir Saffari;Vibhav Vineet

  • Res2Net: A New Multi-Scale Backbone Architecture

    Shang-Hua Gao;Ming-Ming Cheng;Kai Zhao;Xin-Yu Zhang

  • MLESAC: A New Robust Estimator with Application to Estimating Image Geometry

    Philip H. S. Torr;Andrew Zisserman

  • Conditional Random Fields as Recurrent Neural Networks

    Shuai Zheng;Sadeep Jayasumana;Bernardino Romera-Paredes;Vibhav Vineet

  • The Visual Object Tracking VOT2016 Challenge Results

    Matej Kristan;Aleš Leonardis;Jiři Matas;Michael Felsberg

  • Staple: Complementary Learners for Real-Time Tracking

    Luca Bertinetto;Jack Valmadre;Stuart Golodetz;Ondrej Miksik

  • The Visual Object Tracking VOT2017 Challenge Results

    Matej Kristan;Ales Leonardis;Jiri Matas;Michael Felsberg

  • The Visual Object Tracking VOT2015 Challenge Results

    Matej Kristan;Jiri Matas;Ale Leonardis;Michael Felsberg

  • Struck: Structured output tracking with kernels

    Sam Hare;Amir Saffari;Philip H. S. Torr

  • HOTA: A Higher Order Metric for Evaluating Multi-object Tracking.

    Jonathon Luiten;Aljosa Osep;Patrick Dendorfer;Philip H. S. Torr

  • End-to-End Representation Learning for Correlation Filter Based Tracking

    Jack Valmadre;Luca Bertinetto;Joao Henriques;Andrea Vedaldi

  • Fast Online Object Tracking and Segmentation: A Unifying Approach

    Qiang Wang;Li Zhang;Luca Bertinetto;Weiming Hu

  • Deeply Supervised Salient Object Detection with Short Connections

    Qibin Hou;Ming-Ming Cheng;Xiaowei Hu;Ali Borji

  • BING: Binarized Normed Gradients for Objectness Estimation at 300fps

    Ming-Ming Cheng;Ziming Zhang;Wen-Yan Lin;Philip Torr

  • Deeply Supervised Salient Object Detection with Short Connections

    Qibin Hou;Ming-Ming Cheng;Xiaowei Hu;Ali Borji

  • An embarrassingly simple approach to zero-shot learning

    Bernardino Romera-Paredes;Philip Torr

  • Global Contrast Based Salient Region Detection

    Unknown

  • The Development and Comparison of Robust Methodsfor Estimating the Fundamental Matrix

    P. H. S. Torr;D. W. Murray

  • SNIP: SINGLE-SHOT NETWORK PRUNING BASED ON CONNECTION SENSITIVITY

    Namhoon Lee;Thalaiyasingam Ajanthan;Philip H. S. Torr

Frequent Co-Authors

Pushmeet Kohli
Pushmeet Kohli DeepMind (United Kingdom)
Andrew Zisserman
Andrew Zisserman University of Oxford
Ming-Ming Cheng
Ming-Ming Cheng Nankai University
Roberto Cipolla
Roberto Cipolla University of Cambridge
Andrea Vedaldi
Andrea Vedaldi University of Oxford
Carsten Rother
Carsten Rother Heidelberg University
Chris Russell
Chris Russell University of Oxford
Xiaojuan Qi
Xiaojuan Qi University of Hong Kong
Victor Adrian Prisacariu
Victor Adrian Prisacariu University of Oxford
Song Bai
Song Bai ByteDance

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