Mathias Gehrig

729 total citations
15 papers, 355 citations indexed

About

Mathias Gehrig is a scholar working on Electrical and Electronic Engineering, Computer Vision and Pattern Recognition and Computer Networks and Communications. According to data from OpenAlex, Mathias Gehrig has authored 15 papers receiving a total of 355 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Electrical and Electronic Engineering, 6 papers in Computer Vision and Pattern Recognition and 3 papers in Computer Networks and Communications. Recurrent topics in Mathias Gehrig's work include Advanced Memory and Neural Computing (7 papers), Advanced Neural Network Applications (5 papers) and CCD and CMOS Imaging Sensors (2 papers). Mathias Gehrig is often cited by papers focused on Advanced Memory and Neural Computing (7 papers), Advanced Neural Network Applications (5 papers) and CCD and CMOS Imaging Sensors (2 papers). Mathias Gehrig collaborates with scholars based in Switzerland, Germany and Bulgaria. Mathias Gehrig's co-authors include Davide Scaramuzza, Daniel Gehrig, Javier Hidalgo‐Carrió, Wolfgang Kuschinsky, Johannes Vogel, Hugo H. Marti, Marco Cannici, Stanisław Woźniak, Angeliki Pantazi and Luca Benini and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Journal of Cerebral Blood Flow & Metabolism and IEEE Robotics and Automation Letters.

In The Last Decade

Mathias Gehrig

15 papers receiving 345 citations

Peers

Mathias Gehrig

CVPR

EEE

AI

AE

CN

Jijun Tong China

José Ruiz-Pinales Mexico

Kenneth Chaney United States

Vibha Vyas India

Nabeel Khan United Kingdom

Ruihan Hu China

Bernard Girau France

Ziya Telatar Türkiye

Quan Zhou China

Jijun Tong China

Mathias Gehrig

85 ×0.5

CVPR

76 ×0.5

EEE

38 ×0.6

AI

70 ×1.6

AE

82 ×2.0

CN

Citations per year, relative to Mathias Gehrig Mathias Gehrig (= 1×) peers Jijun Tong

Countries citing papers authored by Mathias Gehrig

Since Specialization

Citations

This map shows the geographic impact of Mathias Gehrig'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 Mathias Gehrig with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mathias Gehrig more than expected).

Fields of papers citing papers by Mathias Gehrig

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mathias Gehrig. 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 Mathias Gehrig. The network helps show where Mathias Gehrig may publish in the future.

Co-authorship network of co-authors of Mathias Gehrig

This figure shows the co-authorship network connecting the top 25 collaborators of Mathias Gehrig. A scholar is included among the top collaborators of Mathias Gehrig 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 Mathias Gehrig. Mathias Gehrig is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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15 of 15 papers shown

1.

Gehrig, Mathias, et al.. (2025). Data-Driven Feature Tracking for Event Cameras With and Without Frames. IEEE Transactions on Pattern Analysis and Machine Intelligence. 47(5). 3706–3717. 2 indexed citations

2.

Gehrig, Mathias, et al.. (2024). State Space Models for Event Cameras. 5819–5828. 12 indexed citations

3.

Sun, Lei, Daniel Gehrig, Christos Sakaridis, et al.. (2024). A Unified Framework for Event-Based Frame Interpolation With Ad-Hoc Deblurring in the Wild. IEEE Transactions on Pattern Analysis and Machine Intelligence. 47(4). 2265–2279. 1 indexed citations

4.

Gehrig, Mathias, et al.. (2024). LEOD: Label-Efficient Object Detection for Event Cameras. 16933–16942. 5 indexed citations

5.

Gehrig, Mathias, et al.. (2024). Dense Continuous-Time Optical Flow From Event Cameras. IEEE Transactions on Pattern Analysis and Machine Intelligence. 46(7). 4736–4746. 14 indexed citations

6.

Gehrig, Mathias, et al.. (2024). A Hybrid ANN-SNN Architecture for Low-Power and Low-Latency Visual Perception. 5701–5711. 11 indexed citations

7.

Gehrig, Mathias, et al.. (2024). Revisiting Token Pruning for Object Detection and Instance Segmentation. 2646–2656. 13 indexed citations

8.

Gehrig, Mathias & Davide Scaramuzza. (2023). Recurrent Vision Transformers for Object Detection with Event Cameras. 13884–13893. 79 indexed citations

9.

Gehrig, Mathias, et al.. (2023). Data-Driven Feature Tracking for Event Cameras. 5642–5651. 24 indexed citations

10.

Woźniak, Stanisław, et al.. (2023). Neuromorphic Optical Flow and Real-time Implementation with Event Cameras. Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna). 4129–4138. 12 indexed citations

11.

Gehrig, Daniel, et al.. (2023). From Chaos Comes Order: Ordering Event Representations for Object Recognition and Detection. Zurich Open Repository and Archive (University of Zurich). 12800–12810. 13 indexed citations

12.

Gehrig, Daniel, et al.. (2022). Bridging the Gap Between Events and Frames Through Unsupervised Domain Adaptation. IEEE Robotics and Automation Letters. 7(2). 3515–3522. 32 indexed citations

13.

Gehrig, Daniel, et al.. (2021). Combining Events and Frames Using Recurrent Asynchronous Multimodal Networks for Monocular Depth Prediction. IEEE Robotics and Automation Letters. 6(2). 2822–2829. 94 indexed citations

14.

Gehrig, Daniel, Mathias Gehrig, Javier Hidalgo‐Carrió, & Davide Scaramuzza. (2019). Video to Events: Bringing Modern Computer Vision Closer to Event Cameras.. arXiv (Cornell University). 6 indexed citations

15.

Vogel, Johannes, Mathias Gehrig, Wolfgang Kuschinsky, & Hugo H. Marti. (2004). Massive Inborn Angiogenesis in the Brain Scarcely Raises Cerebral Blood Flow. Journal of Cerebral Blood Flow & Metabolism. 24(8). 849–859. 37 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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