Kyle Feuz
Impact in
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- Context-Aware Activity Recognition Systems
- Human Pose and Action Recognition
- Artificial Intelligence top 5%
- Anomaly Detection Techniques and Applications
- Domain Adaptation and Few-Shot Learning
Papers in
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- Context-Aware Activity Recognition Systems 5
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- Domain Adaptation and Few-Shot Learning 2
- Co-authors
- Diane J. Cook (8 shared papers)Narayanan C. Krishnan (1 shared paper)Maureen Schmitter‐Edgecombe (3 shared papers)Robert Ball (1 shared paper)Daniel W. Cook (1 shared paper)Myriah D. Johnson (2 shared papers)Yong Zhang (1 shared paper)Miles E. Theurer (1 shared paper)
- Journals
- Knowledge and Information Systems (2 papers)Archives of Clinical Neuropsychology (1 paper)Journal of agricultural and resource economics (1 paper)IEEE Transactions on Human-Machine Systems (1 paper)ACM Transactions on Intelligent Systems and Technology (1 paper)
- Partner nations
- United StatesChina
In The Last Decade
Kyle Feuz
13 papers receiving 552 citations
Kyle Feuz's Hit Papers
Peers
Comparison fields: 5 of 97
- Computer Vision and Pattern Recognition 345
- Artificial Intelligence 262
- Computer Science Applications 43
- Transportation 33
- Signal Processing 50
Countries citing papers authored by Kyle Feuz
This map shows the geographic impact of Kyle Feuz'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 Kyle Feuz with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kyle Feuz more than expected).
Fields of papers citing papers by Kyle Feuz
This network shows the impact of papers produced by Kyle Feuz. 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 Kyle Feuz. The network helps show where Kyle Feuz may publish in the future.
Co-authors
The 9 scholars most cited alongside Kyle Feuz, 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 | Transfer learning for activity recognition: a survey Hit paper breakdown → | 2013 | 340 |
| 2 | 2015 | 64 | |
| 3 | 2014 | 47 | |
| 4 | 2017 | 33 | |
| 5 | 2014 | 22 | |
| 6 | 2019 | 15 | |
| 7 | 2015 | 14 | |
| 8 | 2017 | 13 | |
| 9 | Real-Time Annotation Tool (RAT) | 2013 | 6 |
| 10 | 2014 | 4 | |
| 11 | 2020 | 3 | |
| 12 | 2022 | 2 | |
| 13 | 2024 | 1 | |
| 14 | 2024 | 0 | |
| 15 | 2021 | 0 |
About Kyle Feuz
Kyle Feuz is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Computer Networks and Communications, Information Systems and Computer Science Applications, having authored 15 papers that have together received 564 indexed citations. Recurring topics across this work include Context-Aware Activity Recognition Systems (5 papers), Educational Technology and Assessment (3 papers), Human-Automation Interaction and Safety (2 papers), Domain Adaptation and Few-Shot Learning (2 papers), Online Learning and Analytics (2 papers), Software System Performance and Reliability (1 paper), Insurance, Mortality, Demography, Risk Management (1 paper) and Microbial infections and disease research (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (345 citations), Artificial Intelligence (262 citations), Computer Science Applications (43 citations), Transportation (33 citations) and Signal Processing (50 citations). Kyle Feuz has collaborated with scholars based in United States and China. Frequent co-authors include Diane J. Cook, Narayanan C. Krishnan, Maureen Schmitter‐Edgecombe, Robert Ball, Daniel W. Cook, Myriah D. Johnson, Yong Zhang, Yong Zhang and Miles E. Theurer. Their work appears in journals such as Knowledge and Information Systems, Archives of Clinical Neuropsychology, Journal of agricultural and resource economics, IEEE Transactions on Human-Machine Systems and ACM Transactions on Intelligent Systems and Technology.
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.