Wouter Van Gansbeke
Impact in
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- Advanced Neural Network Applications
- Advanced Vision and Imaging
- Medical Image Segmentation Techniques
- Automotive Engineering top 2%
- Autonomous Vehicle Technology and Safety
Papers in
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- Advanced Neural Network Applications 3
- Advanced Vision and Imaging 2
- Generative Adversarial Networks and Image Synthesis 2
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- Domain Adaptation and Few-Shot Learning 3
- Co-authors
- Luc Van Gool (7 shared papers)Stamatios Georgoulis (3 shared papers)Simon Vandenhende (3 shared papers)Marc Proesmans (3 shared papers)Dengxin Dai (2 shared papers)Bert De Brabandere (3 shared papers)Davy Neven (2 shared papers)
- Journals
- IEEE Robotics and Automation Letters (1 paper)IEEE Transactions on Pattern Analysis and Machine Intelligence (1 paper)Lecture notes in computer science (2 papers)arXiv (Cornell University) (2 papers)IEEE/CVF International Conference on Computer Vision (ICCV) (1 paper)
- Partner nations
- BelgiumSwitzerlandBulgaria
In The Last Decade
Wouter Van Gansbeke
8 papers receiving 1.7k citations
Wouter Van Gansbeke's Hit Papers
Peers
Comparison fields: 5 of 91
- Computer Vision and Pattern Recognition 810
- Automotive Engineering 170
- Artificial Intelligence 586
- Instrumentation 16
- Media Technology 56
Countries citing papers authored by Wouter Van Gansbeke
This map shows the geographic impact of Wouter Van Gansbeke'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 Wouter Van Gansbeke with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Wouter Van Gansbeke more than expected).
Fields of papers citing papers by Wouter Van Gansbeke
This network shows the impact of papers produced by Wouter Van Gansbeke. 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 Wouter Van Gansbeke. The network helps show where Wouter Van Gansbeke may publish in the future.
Co-authors
The 7 scholars most cited alongside Wouter Van Gansbeke, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Multi-Task Learning for Dense Prediction Tasks: A Survey Hit paper breakdown → | 2021 | 649 |
| 2 | SCAN: Learning to Classify Images Without Labels Hit paper breakdown → | 2020 | 385 |
| 3 | 2019 | 220 | |
| 4 | 2021 | 190 | |
| 5 | 2019 | 129 | |
| 6 | 2020 | 114 | |
| 7 | 2024 | 6 | |
| 8 | 2024 | 1 |
About Wouter Van Gansbeke
Wouter Van Gansbeke is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Automotive Engineering, Nuclear Energy and Engineering and Waste Management and Disposal, having authored 8 papers that have together received 1.7k indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (3 papers), Advanced Neural Network Applications (3 papers), Advanced Vision and Imaging (2 papers), Generative Adversarial Networks and Image Synthesis (2 papers) and Autonomous Vehicle Technology and Safety (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (810 citations), Automotive Engineering (170 citations), Artificial Intelligence (586 citations), Instrumentation (16 citations) and Media Technology (56 citations). Wouter Van Gansbeke has collaborated with scholars based in Belgium, Switzerland and Bulgaria. Frequent co-authors include Luc Van Gool, Stamatios Georgoulis, Simon Vandenhende, Marc Proesmans, Dengxin Dai, Bert De Brabandere and Davy Neven. Their work appears in journals such as IEEE Robotics and Automation Letters, IEEE Transactions on Pattern Analysis and Machine Intelligence, Lecture notes in computer science, arXiv (Cornell University) and IEEE/CVF International Conference on Computer Vision (ICCV).
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.