Fabian Hüger
- Computer Vision and Pattern Recognition top 10%
- Artificial Intelligence
- Automotive Engineering
- Media Technology
- Electrical and Electronic Engineering
- Co-authors
- Peter SchlichtTim FingscheidtNico M. SchmidtMatthias RottmannHanno GottschalkRobin ChanJán SchneiderMichael Weber
- Topics
- Advanced Neural Network Applications (9 papers)Adversarial Robustness in Machine Learning (6 papers)Anomaly Detection Techniques and Applications (4 papers)
- Journals
- IEEE Signal Processing MagazineATZ - Automobiltechnische Zeitschrift2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
- Partner nations
- GermanyUnited StatesSwitzerland
In The Last Decade
Fabian Hüger
19 papers receiving 191 citations
Peers
Comparison fields: 5 of 51
- Computer Vision and Pattern Recognition 140
- Artificial Intelligence 83
- Automotive Engineering 24
- Media Technology 12
- Electrical and Electronic Engineering 11
Countries citing papers authored by Fabian Hüger
This map shows the geographic impact of Fabian Hüger'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 Fabian Hüger with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fabian Hüger more than expected).
Fields of papers citing papers by Fabian Hüger
This network shows the impact of papers produced by Fabian Hüger. 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 Fabian Hüger. The network helps show where Fabian Hüger may publish in the future.
Co-authorship network of co-authors of Fabian Hüger
This figure shows the co-authorship network connecting the top 25 collaborators of Fabian Hüger. A scholar is included among the top collaborators of Fabian Hüger 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 Fabian Hüger. Fabian Hüger is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | 1 | |
| 3 | 0 | |
| 4 | 3 | |
| 5 | 3 | |
| 6 | 5 | |
| 7 | 7 | |
| 8 | 9 | |
| 9 | 13 | |
| 10 | 16 | |
| 11 | 6 | |
| 12 | 8 | |
| 13 | 22 | |
| 14 | 6 | |
| 15 | 27 | |
| 16 | 6 | |
| 17 | 13 | |
| 18 | 29 | |
| 19 | 16 | |
| 20 | 4 |
About Fabian Hüger
Fabian Hüger is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Software, having authored 20 papers that have together received 197 indexed citations. Recurring topics across this work include Advanced Neural Network Applications (9 papers), Adversarial Robustness in Machine Learning (6 papers) and Anomaly Detection Techniques and Applications (4 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (140 citations), Artificial Intelligence (83 citations) and Automotive Engineering (24 citations). Fabian Hüger has collaborated with scholars based in Germany, United States and Switzerland. Frequent co-authors include Peter Schlicht, Tim Fingscheidt, Nico M. Schmidt, Matthias Rottmann, Hanno Gottschalk, Robin Chan, Ján Schneider, Michael Weber, J. Marius Zöllner and Thomas Stauner. Their work appears in journals such as IEEE Signal Processing Magazine, ATZ - Automobiltechnische Zeitschrift and 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
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.