Aishwarya Agrawal
- Computer Vision and Pattern Recognition top 0.2%
- Artificial Intelligence top 0.5%
- Sociology and Political Science
- Information Systems top 10%
- Signal Processing top 10%
- Co-authors
- Dhruv BatraC. Lawrence ZitnickMargaret MitchellDevi ParikhStanislaw AntolJiasen LuJacob DevlinIshan Misra
- Topics
- Multimodal Machine Learning Applications (12 papers)Domain Adaptation and Few-Shot Learning (9 papers)Advanced Image and Video Retrieval Techniques (6 papers)
- Journals
- International Journal of Computer VisionComputer Vision and Image UnderstandingProceedings of the AAAI Conference on Artificial Intelligence
- Partner nations
- CanadaUnited StatesUnited Kingdom
In The Last Decade
Aishwarya Agrawal
17 papers receiving 2.8k citations
Hit Papers
Peers
Comparison fields: 5 of 108
- Computer Vision and Pattern Recognition 2.5k
- Artificial Intelligence 2.0k
- Sociology and Political Science 77
- Information Systems 76
- Signal Processing 65
Countries citing papers authored by Aishwarya Agrawal
This map shows the geographic impact of Aishwarya Agrawal'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 Agrawal with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Aishwarya Agrawal more than expected).
Fields of papers citing papers by Aishwarya Agrawal
This network shows the impact of papers produced by Aishwarya Agrawal. 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 Agrawal. The network helps show where Aishwarya Agrawal may publish in the future.
Co-authorship network of co-authors of Aishwarya Agrawal
This figure shows the co-authorship network connecting the top 25 collaborators of Aishwarya Agrawal. A scholar is included among the top collaborators of Aishwarya Agrawal 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 Agrawal. Aishwarya Agrawal is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 1 | |
| 3 | 0 | |
| 4 | 0 | |
| 5 | 8 | |
| 6 | 9 | |
| 7 | 1 | |
| 8 | 1 | |
| 9 | 4 | |
| 10 | 7 | |
| 11 | 3 | |
| 12 | 8 | |
| 13 | 4 | |
| 14 | 9 | |
| 15 | 138 | |
| 16 | VQA: Visual Question Answeringbreakdown → | 345 |
| 17 | VQA: Visual Question Answeringbreakdown → | 2331 |
| 18 | 1 | |
| 19 | 3 | |
| 20 | 2 |
About Aishwarya Agrawal
Aishwarya Agrawal is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Radiological and Ultrasound Technology, having authored 21 papers that have together received 2.9k indexed citations. Recurring topics across this work include Multimodal Machine Learning Applications (12 papers), Domain Adaptation and Few-Shot Learning (9 papers) and Advanced Image and Video Retrieval Techniques (6 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (2.5k citations), Artificial Intelligence (2.0k citations) and Health Informatics (20 citations). Aishwarya Agrawal has collaborated with scholars based in Canada, United States and United Kingdom. Frequent co-authors include Dhruv Batra, C. Lawrence Zitnick, Margaret Mitchell, Devi Parikh, Stanislaw Antol, Jiasen Lu, Jacob Devlin, Ishan Misra, Lucy Vanderwende and Xiaodong He. Their work appears in journals such as International Journal of Computer Vision, Computer Vision and Image Understanding and Proceedings of the AAAI Conference on Artificial Intelligence.
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.