Nikhila Ravi
- Computer Vision and Pattern Recognition top 0.5%
- Artificial Intelligence top 1%
- Aerospace Engineering top 5%
- Media Technology top 1%
- Radiology, Nuclear Medicine and Imaging top 5%
- Co-authors
- Alexander C. BergWan‐Yen LoRoss GirshickLaura GustafsonPiotr DollárTete XiaoAlexander M. KirillovHanzi Mao
- Topics
- Advanced Neural Network Applications (4 papers)Visual Attention and Saliency Detection (2 papers)Advanced Vision and Imaging (2 papers)
- Cited by
- Computer Vision and Pattern RecognitionComputer Graphics and Computer-Aided DesignMedia Technology
- Journals
- Indian Journal of Medical Microbiology2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)2021 IEEE/CVF International Conference on Computer Vision (ICCV)
- Partner nations
- United StatesIsraelIndia
In The Last Decade
Nikhila Ravi
9 papers receiving 4.5k citations
Hit Papers
Peers
Comparison fields: 5 of 184
- Computer Vision and Pattern Recognition 2.4k
- Artificial Intelligence 982
- Aerospace Engineering 423
- Media Technology 415
- Radiology, Nuclear Medicine and Imaging 385
Countries citing papers authored by Nikhila Ravi
This map shows the geographic impact of Nikhila Ravi'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 Nikhila Ravi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nikhila Ravi more than expected).
Fields of papers citing papers by Nikhila Ravi
This network shows the impact of papers produced by Nikhila Ravi. 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 Nikhila Ravi. The network helps show where Nikhila Ravi may publish in the future.
Co-authorship network of co-authors of Nikhila Ravi
This figure shows the co-authorship network connecting the top 25 collaborators of Nikhila Ravi. A scholar is included among the top collaborators of Nikhila Ravi 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 Nikhila Ravi. Nikhila Ravi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 40 | |
| 2 | Segment Anythingbreakdown → | 4021 |
| 3 | 9 | |
| 4 | 111 | |
| 5 | 14 | |
| 6 | 44 | |
| 7 | 28 | |
| 8 | Accelerating 3D deep learning with PyTorch3Dbreakdown → | 334 |
| 9 | 15 |
About Nikhila Ravi
Nikhila Ravi is a scholar working on Applied Microbiology and Biotechnology, Computer Vision and Pattern Recognition and Computer Graphics and Computer-Aided Design, having authored 9 papers that have together received 4.6k indexed citations. Recurring topics across this work include Advanced Neural Network Applications (4 papers), Visual Attention and Saliency Detection (2 papers) and Advanced Vision and Imaging (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (2.4k citations), Computer Graphics and Computer-Aided Design (245 citations) and Media Technology (415 citations). Nikhila Ravi has collaborated with scholars based in United States, Israel and India. Frequent co-authors include Alexander C. Berg, Wan‐Yen Lo, Ross Girshick, Laura Gustafson, Piotr Dollár, Tete Xiao, Alexander M. Kirillov, Hanzi Mao, Eric Mintun and Spencer Whitehead. Their work appears in journals such as Indian Journal of Medical Microbiology, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) and 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
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.