Mandela Patrick
Impact in
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- Multimodal Machine Learning Applications
- Human Pose and Action Recognition
- Video Analysis and Summarization
- Advanced Image and Video Retrieval Techniques
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- Domain Adaptation and Few-Shot Learning
- Topic Modeling
- Natural Language Processing Techniques
Papers in
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- Multimodal Machine Learning Applications 4
- Human Pose and Action Recognition 2
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- Domain Adaptation and Few-Shot Learning 2
- Natural Language Processing Techniques 1
- Topic Modeling 1
- Co-authors
- Yuki M. Asano (3 shared papers)Andrea Vedaldi (3 shared papers)Florian Metze (2 shared papers)Po-Yao Huang (2 shared papers)Alexander G. Hauptmann (1 shared paper)Junjie Hu (1 shared paper)Graham Neubig (1 shared paper)João F. Henriques (1 shared paper)
- Journals
- Oxford University Research Archive (ORA) (University of Oxford) (1 paper)2021 IEEE/CVF International Conference on Computer Vision (ICCV) (2 papers)PubMed (1 paper)
- Partner nations
- United KingdomIsraelUnited States
In The Last Decade
Mandela Patrick
5 papers receiving 81 citations
Peers
Comparison fields: 5 of 22
- Computer Vision and Pattern Recognition 66
- Artificial Intelligence 52
- Signal Processing 11
- Cancer Research 4
- Human-Computer Interaction 1
Countries citing papers authored by Mandela Patrick
This map shows the geographic impact of Mandela Patrick'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 Mandela Patrick with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mandela Patrick more than expected).
Fields of papers citing papers by Mandela Patrick
This network shows the impact of papers produced by Mandela Patrick. 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 Mandela Patrick. The network helps show where Mandela Patrick may publish in the future.
Co-authors
The 15 scholars most cited alongside Mandela Patrick, 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 | 2021 | 35 | |
| 2 | 2021 | 31 | |
| 3 | 2021 | 11 | |
| 4 | Labelling unlabelled videos from scratch with multi-modal self-supervision | 2020 | 6 |
| 5 | Intraocular filiariasis (a motion picture). | 1976 | 1 |
About Mandela Patrick
Mandela Patrick is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Clinical Psychology, Physiology and Genetics, having authored 5 papers that have together received 84 indexed citations. Recurring topics across this work include Multimodal Machine Learning Applications (4 papers), Human Pose and Action Recognition (2 papers), Domain Adaptation and Few-Shot Learning (2 papers), Syphilis Diagnosis and Treatment (1 paper), Natural Language Processing Techniques (1 paper), Yersinia bacterium, plague, ectoparasites research (1 paper), Cancer-related molecular mechanisms research (1 paper) and Topic Modeling (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (66 citations), Artificial Intelligence (52 citations), Signal Processing (11 citations), Cancer Research (4 citations) and Human-Computer Interaction (1 citation). Mandela Patrick has collaborated with scholars based in United Kingdom, Israel and United States. Frequent co-authors include Yuki M. Asano, Andrea Vedaldi, Florian Metze, Po-Yao Huang, Alexander G. Hauptmann, Junjie Hu, Graham Neubig, João F. Henriques, Geoffrey Zweig and Полина Кузнецова. Their work appears in journals such as Oxford University Research Archive (ORA) (University of Oxford), 2021 IEEE/CVF International Conference on Computer Vision (ICCV) and PubMed.
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.