Matthias Kümmerer

13.1k total citations
13 papers, 451 citations indexed

About

Matthias Kümmerer is a scholar working on Computer Vision and Pattern Recognition, Cognitive Neuroscience and Sensory Systems. According to data from OpenAlex, Matthias Kümmerer has authored 13 papers receiving a total of 451 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Computer Vision and Pattern Recognition, 5 papers in Cognitive Neuroscience and 4 papers in Sensory Systems. Recurrent topics in Matthias Kümmerer's work include Visual Attention and Saliency Detection (9 papers), Olfactory and Sensory Function Studies (4 papers) and Visual perception and processing mechanisms (3 papers). Matthias Kümmerer is often cited by papers focused on Visual Attention and Saliency Detection (9 papers), Olfactory and Sensory Function Studies (4 papers) and Visual perception and processing mechanisms (3 papers). Matthias Kümmerer collaborates with scholars based in Germany, United Kingdom and Netherlands. Matthias Kümmerer's co-authors include Matthias Bethge, Thomas S. A. Wallis, Leon A. Gatys, Akis Linardos, Christoph Teufel, Jonas Rauber, Wieland Brendel and Jan van Gemert and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Cognition and Journal of Vision.

In The Last Decade

Matthias Kümmerer

13 papers receiving 441 citations

Peers

Matthias Kümmerer

CVPR

CN

HI

SS

AI

Dicky N. Sihite United States

David S. Wooding United Kingdom

Laura Renninger United States

Chengyao Shen Singapore

Honghao Shan United States

Dominique Thoreau France

Umesh Rajashekar United States

S. Ghebreab Netherlands

Lior Elazary United States

L. Itti United States

Dicky N. Sihite United States

Matthias Kümmerer

854 ×2.3

CVPR

353 ×1.7

CN

249 ×1.9

HI

243 ×2.3

SS

38 ×0.9

AI

Citations per year, relative to Matthias Kümmerer Matthias Kümmerer (= 1×) peers Dicky N. Sihite

Countries citing papers authored by Matthias Kümmerer

Since Specialization

Citations

This map shows the geographic impact of Matthias Kümmerer's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Matthias Kümmerer with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matthias Kümmerer more than expected).

Fields of papers citing papers by Matthias Kümmerer

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Matthias Kümmerer. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Matthias Kümmerer. The network helps show where Matthias Kümmerer may publish in the future.

Co-authorship network of co-authors of Matthias Kümmerer

This figure shows the co-authorship network connecting the top 25 collaborators of Matthias Kümmerer. A scholar is included among the top collaborators of Matthias Kümmerer based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Matthias Kümmerer. Matthias Kümmerer is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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13 of 13 papers shown

1.

Kümmerer, Matthias, et al.. (2024). Scale Learning in Scale-Equivariant Convolutional Networks. 567–574. 2 indexed citations

2.

Kümmerer, Matthias & Matthias Bethge. (2023). Predicting Visual Fixations. Annual Review of Vision Science. 9(1). 269–291. 11 indexed citations

3.

Kümmerer, Matthias, Matthias Bethge, & Thomas S. A. Wallis. (2022). DeepGaze III: Modeling free-viewing human scanpaths with deep learning. Journal of Vision. 22(5). 7–7. 41 indexed citations

4.

Kümmerer, Matthias, et al.. (2022). Semantic object-scene inconsistencies affect eye movements, but not in the way predicted by contextualized meaning maps. Journal of Vision. 22(2). 9–9. 3 indexed citations

5.

Linardos, Akis, et al.. (2021). DeepGaze IIE: Calibrated prediction in and out-of-domain for state-of-the-art saliency modeling. 2021 IEEE/CVF International Conference on Computer Vision (ICCV). 12899–12908. 40 indexed citations

6.

Kümmerer, Matthias, et al.. (2020). Meaning maps and saliency models based on deep convolutional neural networks are insensitive to image meaning when predicting human fixations. Cognition. 206. 104465–104465. 17 indexed citations

7.

Brendel, Wieland, et al.. (2019). Accurate, reliable and fast robustness evaluation. MPG.PuRe (Max Planck Society). 32. 12861–12871. 22 indexed citations

8.

Wallis, Thomas S. A., et al.. (2019). Meaning maps and deep neural networks are insensitive to meaning when predicting human fixations. Journal of Vision. 19(10). 253c–253c. 2 indexed citations

9.

Kümmerer, Matthias, Thomas S. A. Wallis, & Matthias Bethge. (2019). DeepGaze III: Using Deep Learning to Probe Interactions Between Scene Content and Scanpath History in Fixation Selection. 6 indexed citations

10.

Kümmerer, Matthias, Thomas S. A. Wallis, & Matthias Bethge. (2018). Extending DeepGaze II: Scanpath prediction from deep features. Journal of Vision. 18(10). 371–371. 2 indexed citations

11.

Kümmerer, Matthias, et al.. (2017). DeepGaze II: Predicting fixations from deep features over time and tasks. Journal of Vision. 17(10). 1147–1147. 33 indexed citations

12.

Kümmerer, Matthias, Thomas S. A. Wallis, Leon A. Gatys, & Matthias Bethge. (2017). Understanding Low- and High-Level Contributions to Fixation Prediction. 4799–4808. 182 indexed citations

13.

Kümmerer, Matthias, Thomas S. A. Wallis, & Matthias Bethge. (2015). Information-theoretic model comparison unifies saliency metrics. Proceedings of the National Academy of Sciences. 112(52). 16054–16059. 90 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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