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Matthias Kümmerer

Active 2014–2026

28
Papers
39,581
Citations
15
h-index
18
i10-index

Citations

Citations per year for Matthias Kümmerer1985: 1 citations1990: 1 citations1992: 1 citations1993: 1 citations1995: 1 citations1997: 1 citations1998: 3 citations1999: 1 citations2000: 1 citations2001: 3 citations2002: 4 citations2003: 3 citations2004: 4 citations2005: 1 citations2006: 1 citations2007: 3 citations2008: 2 citations2009: 4 citations2010: 4 citations2012: 1 citations2013: 3 citations2014: 1 citations2015: 9 citations2016: 28 citations2017: 59 citations2018: 117 citations2019: 135 citations2020: 794 citations2021: 1,498 citations2022: 1,589 citations2023: 1,812 citations2024: 2,322 citations2025: 1,700 citations2026: 373 citations2027: 1 citations1986–1989: no citations, so these years are not shown1991: no citations, so this year is not shown1994: no citations, so this year is not shown1996: no citations, so this year is not shown2011: no citations, so this year is not shown

Citation sources

Countries

World map of the countries and regions citing this authorUnited States: 4,007 citing papers, 23.9% of this breakdownGermany: 1,779 citing papers, 10.6% of this breakdownUnited Kingdom: 1,282 citing papers, 7.6% of this breakdownChina: 879 citing papers, 5.2% of this breakdownFrance: 724 citing papers, 4.3% of this breakdownCanada: 624 citing papers, 3.7% of this breakdownItaly: 545 citing papers, 3.3% of this breakdownSwitzerland: 541 citing papers, 3.2% of this breakdownNetherlands: 514 citing papers, 3.1% of this breakdownSpain: 493 citing papers, 2.9% of this breakdownJapan: 380 citing papers, 2.3% of this breakdownAustralia: 363 citing papers, 2.2% of this breakdown
0%23.9%Other 27.7%

Institutions

Fields

  • Computer Science26.3%
  • Biochemistry, Genetics and Molecular Biology19.4%
  • Medicine12.8%
  • Engineering10.4%
  • Neuroscience7.6%
  • Physics and Astronomy7.3%
  • Other16.2%

Topics

  • Visual Attention and Saliency Detection1.8%
  • Neural dynamics and brain function1.4%
  • Protein Structure and Dynamics1.2%
  • Quantum Computing Algorithms and Architecture1.1%
  • Computational Drug Discovery Methods1.1%
  • Genomics and Phylogenetic Studies1%
  • Other92.4%

Coauthors

All papers

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  1. SciPy 1.0: fundamental algorithms for scientific computing in Python

    Authors: Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, Stéfan J. van der Walt, Matthew Brett, Joshua Wilson, K. Jarrod Millman, Nikolay Mayorov, Andrew R. J. Nelson, Eric Jones, Robert Kern, Eric Larson, C J Carey, İlhan Polat, Yu Feng, Eric W. Moore, Jake VanderPlas, Denis Laxalde, Josef Perktold, Robert Cimrman, Ian Henriksen, E. A. Quintero, Charles R. Harris, Anne M. Archibald, Antônio H. Ribeiro, Fabian Pedregosa, Paul van Mulbregt, SciPy 1.0 Contributors, Aditya Vijaykumar, Alessandro Pietro Bardelli, Alex Rothberg, Andreas Hilboll, Andreas Kloeckner, Anthony Scopatz, Antony Lee, Ariel Rokem, C. Nathan Woods, Chad Fulton, Charles Masson, Christian Häggström, Clark Fitzgerald, David A. Nicholson, David R. Hagen, Dmitrii V. Pasechnik, Emanuele Olivetti, Eric Martin, Eric Wieser, Fabrice Silva, Felix Lenders, Florian Wilhelm, G. Young, Gavin A. Price, Gert-Ludwig Ingold, Gregory E. Allen, Gregory R. Lee, Hervé Audren, Irvin Probst, Jörg P. Dietrich, Jacob Silterra, James T Webber, Janko Slavič, Joel Nothman, Johannes Buchner, Johannes Kulick, Johannes L. Schönberger, José Vinícius de Miranda Cardoso, Joscha Reimer, Joseph Harrington, Juan Luis Cano Rodríguez, Juan Nunez-Iglesias, Justin Kuczynski, Kevin Tritz, Martin Thoma, Matthew Newville, Matthias Kümmerer, Maximilian Bolingbroke, Michael Tartre, Mikhail Pak, Nathaniel J. Smith, Nikolai Nowaczyk, Nikolay Shebanov, Oleksandr Pavlyk, Per A. Brodtkorb, Perry Lee, Robert T. McGibbon, Roman Feldbauer, Sam Lewis, Sam Tygier, Scott Sievert, Sebastiano Vigna, Stefan Peterson, Surhud More, Tadeusz Pudlik and 12 more - Nature Methods 2020 cited by 37,936

  2. DeepGaze III: Modeling free-viewing human scanpaths with deep learning

    Authors: Matthias Kümmerer, Matthias Bethge, Thomas S. A. Wallis - Journal of Vision 2022 cited by 104

  3. DeepGaze II: Reading fixations from deep features trained on object recognition

    Authors: Matthias Kümmerer, Thomas S. A. Wallis, Matthias Bethge - arXiv (Cornell University), CoRR 2016 cited by 262

  4. Deep Gaze I: Boosting Saliency Prediction with Feature Maps Trained on ImageNet

    Authors: Matthias Kümmerer, Lucas Theis, Matthias Bethge - ICLR (Workshop) 2015 cited by 231

  5. Understanding Low- and High-Level Contributions to Fixation Prediction

    Authors: Matthias Kümmerer, Thomas S. A. Wallis, Leon A. Gatys, Matthias Bethge - IEEE International Conference on Computer Vision (ICCV) 2017 cited by 291

  6. Information-theoretic model comparison unifies saliency metrics

    Authors: Matthias Kümmerer, Thomas S. A. Wallis, Matthias Bethge - National Academy of Sciences, Proceedings of the National Academy of Sciences 2015 cited by 162

  7. State-of-the-Art in Human Scanpath Prediction

    Authors: Matthias Kümmerer, Matthias Bethge - arXiv (Cornell University), CoRR 2021 cited by 29

  8. DeepGaze II: Predicting fixations from deep features over time and tasks

    Authors: Matthias Kümmerer, Tom Wallis, Matthias Bethge - Journal of Vision 2017 cited by 71

  9. Predicting Visual Fixations

    Authors: Matthias Kümmerer, Matthias Bethge - Annual Review of Vision Science 2023 cited by 23

  10. Unsupervised Object Learning via Common Fate

    Authors: Matthias Tangemann, Steffen Schneider, Julius von Kügelgen, Francesco Locatello, Peter Vincent Gehler, Thomas Brox, Matthias Kümmerer, Matthias Bethge, Bernhard Schölkopf - CLeaR 2023 cited by 20

  11. DeepGaze IIE: Calibrated prediction in and out-of-domain for state-of-the-art saliency modeling

    Authors: Akis Linardos, Matthias Kümmerer, Ori Press, Matthias Bethge - IEEE/CVF International Conference on Computer Vision (ICCV) 2021 cited by 85

  12. Accurate, reliable and fast robustness evaluation

    Authors: Wieland Brendel, Jonas Rauber, Matthias Kümmerer, Ivan Ustyuzhaninov, Matthias Bethge - NeurIPS 2019 cited by 118

  13. Meaning maps and saliency models based on deep convolutional neural networks are insensitive to image meaning when predicting human fixations

    Authors: Marek A. Pedziwiatr, Matthias Kümmerer, Thomas S. A. Wallis, Matthias Bethge, Christoph Teufel - Cognition 2020 cited by 32

  14. DeepGaze III: Using Deep Learning to Probe Interactions Between Scene Content and Scanpath History in Fixation Selection

    Authors: Matthias Kümmerer, Thomas S. A. Wallis, Matthias Bethge - Conference on Cognitive Computational Neuroscience 2019 cited by 13

  15. Scale Learning in Scale-Equivariant Convolutional Networks

    Authors: Mark Basting, Robert-Jan Bruintjes, Thaddäus Wiedemer, Matthias Kümmerer, Matthias Bethge, Jan van Gemert - Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP (2): VISAPP 2024 cited by 3

  16. RDumb: A simple approach that questions our progress in continual test-time adaptation

    Authors: Ori Press, Steffen Schneider, Matthias Kümmerer, Matthias Bethge - Advances in Neural Information Processing Systems 36, NeurIPS 2023 cited by 61

  17. Saliency Benchmarking Made Easy: Separating Models, Maps and Metrics

    Authors: Matthias Kümmerer, Thomas S. A. Wallis, Matthias Bethge - Lecture notes in computer science, ECCV (16) 2018 cited by 101

  18. Guiding human gaze with convolutional neural networks

    Authors: Leon A. Gatys, Matthias Kümmerer, Thomas S. A. Wallis, Matthias Bethge - arXiv (Cornell University), CoRR 2017 cited by 13

  19. Measuring the Importance of Temporal Features in Video Saliency

    Authors: Matthias Tangemann, Matthias Kümmerer, Thomas S. A. Wallis, Matthias Bethge - Journal of Vision, ECCV (28) 2020 cited by 12

  20. Object segmentation from common fate: Motion energy processing enables human-like zero-shot generalization to random dot stimuli

    Authors: Matthias Tangemann, Matthias Kümmerer, Matthias Bethge - Advances in Neural Information Processing Systems 37, NeurIPS 2024 cited by 6

  21. Modeling Saliency Dataset Bias

    Authors: Matthias Kümmerer, Harneet Singh Khanuja, Matthias Bethge - IEEE/CVF International Conference on Computer Vision (ICCV) 2025 cited by 1

  22. Disentanglement and Generalization Under Correlation Shifts

    Authors: Christina M. Funke, Paul Vicol, Kuan-Chieh Wang, Matthias Kümmerer, Richard S. Zemel, Matthias Bethge - CoLLAs 2022 cited by 1

  23. How close are we to understanding image-based saliency?

    Authors: Matthias Kümmerer, Thomas Wallis, Matthias Bethge - arXiv (Cornell University), CoRR 2014 cited by 6

  24. DeepGaze3.5-VL: Modeling Scanpaths via Autoregressive Token Prediction

    Authors: Susmit Agrawal, Matthias Bethge, Matthias Kümmerer - CoRR 2026 cited by 0