Jason Ramapuram
- Computer Vision and Pattern Recognition top 5%
- Artificial Intelligence
- Aerospace Engineering
- Geology
- Computer Graphics and Computer-Aided Design top 10%
- Co-authors
- Alexandros KalousisAnurag RanjanRuss WebbMike RobertsJoshua M. SusskindMiguel Ángel BautistaJeffrey De FauwJean-Baptiste Alayrac
- Topics
- Domain Adaptation and Few-Shot Learning (2 papers)Multimodal Machine Learning Applications (2 papers)Advanced Vision and Imaging (1 paper)
- Journals
- Neurocomputing2021 IEEE/CVF International Conference on Computer Vision (ICCV)Neural Information Processing Systems
- Partner nations
- SwitzerlandUnited States
In The Last Decade
Jason Ramapuram
3 papers receiving 201 citations
Peers
Comparison fields: 5 of 36
- Computer Vision and Pattern Recognition 157
- Artificial Intelligence 75
- Aerospace Engineering 39
- Geology 26
- Computer Graphics and Computer-Aided Design 24
Countries citing papers authored by Jason Ramapuram
This map shows the geographic impact of Jason Ramapuram'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 Jason Ramapuram with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jason Ramapuram more than expected).
Fields of papers citing papers by Jason Ramapuram
This network shows the impact of papers produced by Jason Ramapuram. 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 Jason Ramapuram. The network helps show where Jason Ramapuram may publish in the future.
Co-authorship network of co-authors of Jason Ramapuram
This figure shows the co-authorship network connecting the top 25 collaborators of Jason Ramapuram. A scholar is included among the top collaborators of Jason Ramapuram 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 Jason Ramapuram. Jason Ramapuram is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 143 | |
| 2 | Self-Supervised MultiModal Versatile Networks | 7 |
| 3 | 60 |
About Jason Ramapuram
Jason Ramapuram is a scholar working on Geology, Computer Vision and Pattern Recognition and Artificial Intelligence, having authored 3 papers that have together received 210 indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (2 papers), Multimodal Machine Learning Applications (2 papers) and Advanced Vision and Imaging (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (157 citations), Computer Graphics and Computer-Aided Design (24 citations) and Geology (26 citations). Jason Ramapuram has collaborated with scholars based in Switzerland and United States. Frequent co-authors include Alexandros Kalousis, Anurag Ranjan, Russ Webb, Mike Roberts, Joshua M. Susskind, Miguel Ángel Bautista, Jeffrey De Fauw, Jean-Baptiste Alayrac, Relja Arandjelović and Sander Dieleman. Their work appears in journals such as Neurocomputing, 2021 IEEE/CVF International Conference on Computer Vision (ICCV) and Neural Information Processing Systems.
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.