Zsolt Kira
- Computer Vision and Pattern Recognition top 0.5%
- Artificial Intelligence top 2%
- Aerospace Engineering top 5%
- Control and Systems Engineering top 5%
- Geology top 5%
- Co-authors
- Chih‐Yao MaGhassan AlRegibYen‐Cheng LiuChia-Wen KuoJunjiao TianMin-Hung ChenYong K. ChoJingdao Chen
- Topics
- Domain Adaptation and Few-Shot Learning (23 papers)Multimodal Machine Learning Applications (22 papers)Advanced Neural Network Applications (15 papers)
- Journals
- SAE technical papers on CD-ROM/SAE technical paper seriesIEEE Robotics and Automation LettersJournal of Computing in Civil Engineering
- Partner nations
- United StatesSlovakiaUnited Kingdom
In The Last Decade
Zsolt Kira
62 papers receiving 1.8k citations
Peers
Comparison fields: 5 of 101
- Computer Vision and Pattern Recognition 1.2k
- Artificial Intelligence 765
- Aerospace Engineering 245
- Control and Systems Engineering 158
- Geology 151
Countries citing papers authored by Zsolt Kira
This map shows the geographic impact of Zsolt Kira'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 Zsolt Kira with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Zsolt Kira more than expected).
Fields of papers citing papers by Zsolt Kira
This network shows the impact of papers produced by Zsolt Kira. 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 Zsolt Kira. The network helps show where Zsolt Kira may publish in the future.
Co-authorship network of co-authors of Zsolt Kira
This figure shows the co-authorship network connecting the top 25 collaborators of Zsolt Kira. A scholar is included among the top collaborators of Zsolt Kira 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 Zsolt Kira. Zsolt Kira is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 1 | |
| 3 | 4 | |
| 4 | 1 | |
| 5 | 4 | |
| 6 | 26 | |
| 7 | 1 | |
| 8 | 81 | |
| 9 | 139 | |
| 10 | 7 | |
| 11 | Self-Monitoring Navigation Agent via Auxiliary Progress Estimation | 26 |
| 12 | How to Train Your DRAGAN | 12 |
| 13 | Learning to cluster in order to transfer across domains and tasks | 17 |
| 14 | 101 | |
| 15 | 57 | |
| 16 | 8 | |
| 17 | 11 | |
| 18 | 6 | |
| 19 | Mapping Grounded Object Properties Across Perceptually Heterogeneous Embodiments | 4 |
| 20 | 60 |
About Zsolt Kira
Zsolt Kira is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Geology, having authored 68 papers that have together received 1.9k indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (23 papers), Multimodal Machine Learning Applications (22 papers) and Advanced Neural Network Applications (15 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (1.2k citations), Geology (151 citations) and Artificial Intelligence (765 citations). Zsolt Kira has collaborated with scholars based in United States, Slovakia and United Kingdom. Frequent co-authors include Chih‐Yao Ma, Ghassan AlRegib, Yen‐Cheng Liu, Chia-Wen Kuo, Junjiao Tian, Min-Hung Chen, Yong K. Cho, Jingdao Chen, James Smith and Yen-Chang Hsu. Their work appears in journals such as SAE technical papers on CD-ROM/SAE technical paper series, IEEE Robotics and Automation Letters and Journal of Computing in Civil Engineering.
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.