Alexander Gepperth
Impact in
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- Video Surveillance and Tracking Methods
- Multimodal Machine Learning Applications
- Advanced Neural Network Applications
- Human Pose and Action Recognition
- Automotive Engineering top 10%
- Autonomous Vehicle Technology and Safety
Papers in
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- Domain Adaptation and Few-Shot Learning 9
- Neural Networks and Applications 8
- Data Stream Mining Techniques 4
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- Video Surveillance and Tracking Methods 10
- Advanced Neural Network Applications 8
- Visual Attention and Saliency Detection 5
- Co-authors
- M. Ortíz (4 shared papers)Jannik Fritsch (8 shared papers)Franz Kümmert (2 shared papers)Stefan Roth (2 shared papers)Johann Edelbrunner (1 shared paper)Uwe Handmann (3 shared papers)Christian Goerick (6 shared papers)Bernd Heisele (1 shared paper)
In The Last Decade
Alexander Gepperth
42 papers receiving 400 citations
Peers
Comparison fields: 5 of 57
- Computer Vision and Pattern Recognition 191
- Automotive Engineering 85
- Artificial Intelligence 205
- Human-Computer Interaction 27
- Safety, Risk, Reliability and Quality 26
Countries citing papers authored by Alexander Gepperth
This map shows the geographic impact of Alexander Gepperth'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 Alexander Gepperth with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Alexander Gepperth more than expected).
Fields of papers citing papers by Alexander Gepperth
This network shows the impact of papers produced by Alexander Gepperth. 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 Alexander Gepperth. The network helps show where Alexander Gepperth may publish in the future.
Co-authors
The 25 scholars most cited alongside Alexander Gepperth, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 46 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2016 | 91 | |
| 2 | 2020 | 45 | |
| 3 | 2011 | 37 | |
| 4 | 2019 | 34 | |
| 5 | 2008 | 22 | |
| 6 | 2005 | 20 | |
| 7 | 2019 | 18 | |
| 8 | 2011 | 13 | |
| 9 | 2006 | 12 | |
| 10 | 2013 | 11 | |
| 11 | 2017 | 11 | |
| 12 | 2008 | 8 | |
| 13 | 2021 | 8 | |
| 14 | 2012 | 8 | |
| 15 | 2010 | 8 | |
| 16 | 2016 | 7 | |
| 17 | 2008 | 7 | |
| 18 | 2011 | 6 | |
| 19 | 2023 | 6 | |
| 20 | 2012 | 5 |
About Alexander Gepperth
Alexander Gepperth is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Cognitive Neuroscience, Human-Computer Interaction and Automotive Engineering, having authored 46 papers that have together received 415 indexed citations. Recurring topics across this work include Video Surveillance and Tracking Methods (10 papers), Domain Adaptation and Few-Shot Learning (9 papers), Neural Networks and Applications (8 papers), Advanced Neural Network Applications (8 papers), Visual Attention and Saliency Detection (5 papers), Visual perception and processing mechanisms (5 papers), Autonomous Vehicle Technology and Safety (4 papers) and Data Stream Mining Techniques (4 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (191 citations), Automotive Engineering (85 citations), Artificial Intelligence (205 citations), Human-Computer Interaction (27 citations) and Safety, Risk, Reliability and Quality (26 citations). Alexander Gepperth has collaborated with scholars based in Germany, France and Japan. Frequent co-authors include M. Ortíz, Jannik Fritsch, Franz Kümmert, Stefan Roth, Johann Edelbrunner, Uwe Handmann, Christian Goerick, Bernd Heisele, Marcus Kleinehagenbrock and Maryam Bahrami. Their work appears in journals such as Cognitive Computation, Neural Processing Letters, Neurocomputing, BMC Bioinformatics and Neural Networks.
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.