Debidatta Dwibedi
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 5%
- Control and Systems Engineering
- Biomedical Engineering
- Signal Processing
- Co-authors
- Jonathan TompsonPierre SermanetYusuf AytarAndrew ZissermanCorey LynchKumar Krishna AgrawalIlya KostrikovSergey Levine
- Topics
- Multimodal Machine Learning Applications (5 papers)Human Pose and Action Recognition (3 papers)Domain Adaptation and Few-Shot Learning (3 papers)
- Journals
- 2021 IEEE/CVF International Conference on Computer Vision (ICCV)arXiv (Cornell University)Computer Vision and Pattern Recognition
- Partner nations
- United StatesUnited Kingdom
In The Last Decade
Debidatta Dwibedi
9 papers receiving 401 citations
Hit Papers
Peers
Comparison fields: 5 of 84
- Artificial Intelligence 258
- Computer Vision and Pattern Recognition 245
- Control and Systems Engineering 47
- Biomedical Engineering 25
- Signal Processing 23
Countries citing papers authored by Debidatta Dwibedi
This map shows the geographic impact of Debidatta Dwibedi'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 Debidatta Dwibedi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Debidatta Dwibedi more than expected).
Fields of papers citing papers by Debidatta Dwibedi
This network shows the impact of papers produced by Debidatta Dwibedi. 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 Debidatta Dwibedi. The network helps show where Debidatta Dwibedi may publish in the future.
Co-authorship network of co-authors of Debidatta Dwibedi
This figure shows the co-authorship network connecting the top 25 collaborators of Debidatta Dwibedi. A scholar is included among the top collaborators of Debidatta Dwibedi 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 Debidatta Dwibedi. Debidatta Dwibedi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 25 | |
| 2 | 11 | |
| 3 | 4 | |
| 4 | 1 | |
| 5 | With a Little Help from My Friends: Nearest-Neighbor Contrastive Learning of Visual Representationsbreakdown → | 245 |
| 6 | 67 | |
| 7 | Temporal Reasoning in Videos Using Convolutional Gated Recurrent Units | 12 |
| 8 | 24 | |
| 9 | 27 |
About Debidatta Dwibedi
Debidatta Dwibedi is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Management Information Systems, having authored 9 papers that have together received 416 indexed citations. Recurring topics across this work include Multimodal Machine Learning Applications (5 papers), Human Pose and Action Recognition (3 papers) and Domain Adaptation and Few-Shot Learning (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (245 citations), Artificial Intelligence (258 citations) and Signal Processing (23 citations). Debidatta Dwibedi has collaborated with scholars based in United States and United Kingdom. Frequent co-authors include Jonathan Tompson, Pierre Sermanet, Yusuf Aytar, Andrew Zisserman, Corey Lynch, Kumar Krishna Agrawal, Ilya Kostrikov, Sergey Levine, Quan Vuong and Dorsa Sadigh. Their work appears in journals such as 2021 IEEE/CVF International Conference on Computer Vision (ICCV), arXiv (Cornell University) and Computer Vision and Pattern Recognition.
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.