World's Best Scientists 2026 revealed!
Andrew D. Bagdanov

Andrew D. Bagdanov

D-Index & Metrics

Computer Science

D-Index
35
Citations
8053
World Ranking
11477
National Ranking
376

Andrew D. Bagdanov 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 Andrew D. Bagdanov 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: 156 publications — 29th percentile

29% of scientists in this discipline score the same or lower.

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

Andrew D. Bagdanov 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 Andrew D. Bagdanov 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: 35 D-Index — 20th percentile

20% of scientists in this discipline score the same or lower.

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

Overview

Andrew D. Bagdanov is affiliated with the University of Florence in Italy. Their research spans the field of computer science, with a particular focus on artificial intelligence and computer vision.

The scientist has contributed to various subfields of study, including:

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Radiology, Nuclear Medicine and Imaging
  • Sociology and Political Science
  • Cancer Research

Their recent published papers include:

  • Class-Incremental Learning: Survey and Performance Evaluation on Image Classification, 2022, IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Class-incremental learning: survey and performance evaluation on image classification, 2020, arXiv (Cornell University)
  • Bottom-up and Layerwise Domain Adaptation for Pedestrian Detection in Thermal Images, 2021, ACM Transactions on Multimedia Computing Communications and Applications
  • Visual Question Answering for Cultural Heritage, 2020, IOP Conference Series Materials Science and Engineering
  • Continually Learning Self-Supervised Representations with Projected Functional Regularization, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Frequent coauthors collaborating with Andrew D. Bagdanov include:

  • Joost van de Weijer
  • Bartłomiej Twardowski
  • Alessandro Sestini
  • Xialei Liu
  • Simone Magistri

The scientist has published in several venues, with the most frequent being:

  • arXiv (Cornell University)
  • 2021 IEEE Conference on Games (CoG)
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • ACM Transactions on Multimedia Computing Communications and Applications
  • IOP Conference Series Materials Science and Engineering

The main topics covered in their research work include:

  • Domain Adaptation and Few-Shot Learning
  • Artificial Intelligence in Games
  • Reinforcement Learning in Robotics
  • Multimodal Machine Learning Applications
  • Machine Learning and ELM
  • Advanced Neural Network Applications
  • Digital Games and Media

Best Publications

  • ICDAR 2015 competition on Robust Reading

    Dimosthenis Karatzas;Lluis Gomez-Bigorda;Anguelos Nicolaou;Suman Ghosh

  • Class-incremental learning: survey and performance evaluation on image classification

    Marc Masana;Xialei Liu;Bartlomiej Twardowski;Mikel Menta

  • RankIQA: Learning from Rankings for No-Reference Image Quality Assessment

    Xialei Liu;Joost van de Weijer;Andrew D. Bagdanov

  • Boosting color saliency in image feature detection

    J. van de Weijer;T. Gevers;A.D. Bagdanov

  • Person Re-Identification by Iterative Re-Weighted Sparse Ranking

    Giuseppe Lisanti;Iacopo Masi;Andrew D. Bagdanov;Alberto Del Bimbo

  • Color attributes for object detection

    Fahad Shahbaz Khan;Rao Muhammad Anwer;Joost van de Weijer;Andrew D. Bagdanov

  • Leveraging Unlabeled Data for Crowd Counting by Learning to Rank

    Xialei Liu;Joost van de Weijer;Andrew D. Bagdanov

  • Rotate your Networks: Better Weight Consolidation and Less Catastrophic Forgetting

    Xialei Liu;Marc Masana;Luis Herranz;Joost Van de Weijer

  • Harmony Potentials

    Xavier Boix;Josep M. Gonfaus;Joost Weijer;Andrew D. Bagdanov

  • Harmony potentials for joint classification and segmentation

    Josep M. Gonfaus;Xavier Boix;Joost van de Weijer;Andrew D. Bagdanov

  • Exploiting Unlabeled Data in CNNs by Self-Supervised Learning to Rank

    Xialei Liu;Joost van de Weijer;Andrew D. Bagdanov

  • The florence 2D/3D hybrid face dataset

    Andrew D. Bagdanov;Alberto Del Bimbo;Iacopo Masi

  • Coloring Action Recognition in Still Images

    Fahad Shahbaz Khan;Rao Muhammad Anwer;Joost Weijer;Andrew D. Bagdanov

  • Trademark matching and retrieval in sports video databases

    Andrew D. Bagdanov;Lamberto Ballan;Marco Bertini;Alberto Del Bimbo

  • Generative Feature Replay For Class-Incremental Learning

    Xialei Liu;Chenshen Wu;Mikel Menta;Luis Herranz

  • Semantic annotation and retrieval of video events using multimedia ontologies

    A.D. Bagdanov;M. Bertini;A. Del Bimbo;G. Serra

  • Accurate Moving Cast Shadow Suppression Based on Local Color Constancy Detection

    A. Amato;M. G. Mozerov;A. D. Bagdanov;J. Gonzalez

  • Sparse radial sampling LBP for writer identification

    Anguelos Nicolaou;Andrew D. Bagdanov;Marcus Liwicki;Dimosthenis Karatzas

  • Color Feature Detection

    Theo Gevers;Joost van de Weijer;Harro Stokman

  • Portmanteau Vocabularies for Multi-Cue Image Representation

    Fahad S. Khan;Joost Weijer;Andrew D. Bagdanov;Maria Vanrell

  • First Order Gaussian Graphs for Efficient Structure Classification

    Andrew D. Bagdanov;Marcel Worring

  • Projection profile based skew estimation algorithm for JBIG compressed images

    A. Bagdanov;J. Kanai

  • Class-incremental learning: survey and performance evaluation

    Marc Masana;Xialei Liu;Bartlomiej Twardowski;Mikel Menta

Frequent Co-Authors

Alberto Del Bimbo
Alberto Del Bimbo University of Florence
Joost van de Weijer
Joost van de Weijer Autonomous University of Barcelona
Marco Bertini
Marco Bertini University of Florence
Jordi Gonzàlez
Jordi Gonzàlez Autonomous University of Barcelona
Dimosthenis Karatzas
Dimosthenis Karatzas Autonomous University of Barcelona
Marcel Worring
Marcel Worring University of Amsterdam
Fahad Shahbaz Khan
Fahad Shahbaz Khan Mohamed bin Zayed University of Artificial Intelligence
Rao Muhammad Anwer
Rao Muhammad Anwer Aalto University
Michael Felsberg
Michael Felsberg Linköping University
Antonio M. López
Antonio M. López Autonomous University of Barcelona

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