Jonathan Munro
Impact in
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- Human Pose and Action Recognition
- Multimodal Machine Learning Applications
- Video Surveillance and Tracking Methods
- Video Analysis and Summarization
- Context-Aware Activity Recognition Systems
- Advanced Neural Network Applications
Papers in
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- Human Pose and Action Recognition 2
- Multimodal Machine Learning Applications 2
- Face and Expression Recognition 1
- Video Surveillance and Tracking Methods 1
- Video Analysis and Summarization 1
- Face recognition and analysis 1
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- Domain Adaptation and Few-Shot Learning 1
- Co-authors
- Giovanni Maria Farinella (2 shared papers)Dima Damen (2 shared papers)Toby Perrett (2 shared papers)Evangelos Kazakos (2 shared papers)Michael Wray (2 shared papers)Antonino Furnari (2 shared papers)Will Price (2 shared papers)Hazel Doughty (2 shared papers)
- Journals
- International Journal of Computer Vision (1 paper)IEEE Transactions on Pattern Analysis and Machine Intelligence (1 paper)Explore Bristol Research (1 paper)
- Partner nations
- United KingdomItalyNetherlands
In The Last Decade
Jonathan Munro
3 papers receiving 286 citations
Hit Papers
Peers
Comparison fields: 5 of 46
- Computer Vision and Pattern Recognition 257
- Human-Computer Interaction 23
- Artificial Intelligence 129
- Signal Processing 27
- Control and Systems Engineering 23
Countries citing papers authored by Jonathan Munro
This map shows the geographic impact of Jonathan Munro'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 Jonathan Munro with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jonathan Munro more than expected).
Fields of papers citing papers by Jonathan Munro
This network shows the impact of papers produced by Jonathan Munro. 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 Jonathan Munro. The network helps show where Jonathan Munro may publish in the future.
Co-authors
The 13 scholars most cited alongside Jonathan Munro, 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 | Rescaling Egocentric Vision: Collection, Pipeline and Challenges for EPIC-KITCHENS-100 Hit paper breakdown → | 2021 | 188 |
| 2 | 2020 | 104 | |
| 3 | 2017 | 10 |
About Jonathan Munro
Jonathan Munro is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Infectious Diseases, Organic Chemistry and Surgery, having authored 3 papers that have together received 302 indexed citations. Recurring topics across this work include Human Pose and Action Recognition (2 papers), Multimodal Machine Learning Applications (2 papers), Domain Adaptation and Few-Shot Learning (1 paper), Face and Expression Recognition (1 paper), Video Surveillance and Tracking Methods (1 paper), Video Analysis and Summarization (1 paper) and Face recognition and analysis (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (257 citations), Human-Computer Interaction (23 citations), Artificial Intelligence (129 citations), Signal Processing (27 citations) and Control and Systems Engineering (23 citations). Jonathan Munro has collaborated with scholars based in United Kingdom, Italy and Netherlands. Frequent co-authors include Giovanni Maria Farinella, Dima Damen, Toby Perrett, Evangelos Kazakos, Michael Wray, Antonino Furnari, Will Price, Hazel Doughty, Davide Moltisanti and Jian Ma. Their work appears in journals such as International Journal of Computer Vision, IEEE Transactions on Pattern Analysis and Machine Intelligence and Explore Bristol Research.
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.