Aishwarya Kamath
- Computer Vision and Pattern Recognition top 2%
- Artificial Intelligence top 5%
- Aerospace Engineering
- Control and Systems Engineering
- Electrical and Electronic Engineering
- Co-authors
- Nicolas CarionYann LeCunGabriel SynnaeveIshan MisraMannat SinghJonas PfeifferRajarshi DasStefan Roth
- Topics
- Multimodal Machine Learning Applications (6 papers)Topic Modeling (5 papers)Domain Adaptation and Few-Shot Learning (4 papers)
- Journals
- 2021 IEEE/CVF International Conference on Computer Vision (ICCV)Edinburgh Research ExplorerarXiv (Cornell University)
- Partner nations
- United StatesGermanyUnited Kingdom
In The Last Decade
Aishwarya Kamath
7 papers receiving 436 citations
Hit Papers
Peers
Comparison fields: 5 of 49
- Computer Vision and Pattern Recognition 364
- Artificial Intelligence 264
- Aerospace Engineering 14
- Control and Systems Engineering 12
- Electrical and Electronic Engineering 9
Countries citing papers authored by Aishwarya Kamath
This map shows the geographic impact of Aishwarya Kamath'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 Aishwarya Kamath with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Aishwarya Kamath more than expected).
Fields of papers citing papers by Aishwarya Kamath
This network shows the impact of papers produced by Aishwarya Kamath. 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 Aishwarya Kamath. The network helps show where Aishwarya Kamath may publish in the future.
Co-authorship network of co-authors of Aishwarya Kamath
This figure shows the co-authorship network connecting the top 25 collaborators of Aishwarya Kamath. A scholar is included among the top collaborators of Aishwarya Kamath 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 Aishwarya Kamath. Aishwarya Kamath is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 20 | |
| 2 | 5 | |
| 3 | 22 | |
| 4 | MDETR - Modulated Detection for End-to-End Multi-Modal Understandingbreakdown → | 379 |
| 5 | 10 | |
| 6 | 1 | |
| 7 | 7 | |
| 8 | 7 |
About Aishwarya Kamath
Aishwarya Kamath is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Modeling and Simulation, having authored 8 papers that have together received 451 indexed citations. Recurring topics across this work include Multimodal Machine Learning Applications (6 papers), Topic Modeling (5 papers) and Domain Adaptation and Few-Shot Learning (4 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (364 citations), Artificial Intelligence (264 citations) and Geology (7 citations). Aishwarya Kamath has collaborated with scholars based in United States, Germany and United Kingdom. Frequent co-authors include Nicolas Carion, Yann LeCun, Gabriel Synnaeve, Ishan Misra, Mannat Singh, Jonas Pfeiffer, Rajarshi Das, Stefan Roth, Iryna Gurevych and Edoardo Maria Ponti. Their work appears in journals such as 2021 IEEE/CVF International Conference on Computer Vision (ICCV), Edinburgh Research Explorer and arXiv (Cornell University).
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.