Vidit Jain
- Computer Vision and Pattern Recognition top 1%
- Signal Processing top 5%
- Artificial Intelligence top 10%
- Information Systems top 10%
- Sociology and Political Science
- Co-authors
- Erik Learned-MillerGary B. HuangManik VarmaRohit Kumar KaliyarAndrew McCallumAnurag GoswamiPratik NarangYashvardhan Sharma
- Topics
- Advanced Image and Video Retrieval Techniques (6 papers)Text and Document Classification Technologies (4 papers)Topic Modeling (3 papers)
- Journals
- Neural Computing and ApplicationsProceedings of the VLDB EndowmentLecture notes in computer science
- Partner nations
- United StatesIndiaUnited Kingdom
In The Last Decade
Vidit Jain
13 papers receiving 1.0k citations
Hit Papers
Peers
Comparison fields: 5 of 86
- Computer Vision and Pattern Recognition 934
- Signal Processing 199
- Artificial Intelligence 199
- Information Systems 55
- Sociology and Political Science 43
Countries citing papers authored by Vidit Jain
This map shows the geographic impact of Vidit Jain'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 Vidit Jain with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Vidit Jain more than expected).
Fields of papers citing papers by Vidit Jain
This network shows the impact of papers produced by Vidit Jain. 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 Vidit Jain. The network helps show where Vidit Jain may publish in the future.
Co-authorship network of co-authors of Vidit Jain
This figure shows the co-authorship network connecting the top 25 collaborators of Vidit Jain. A scholar is included among the top collaborators of Vidit Jain 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 Vidit Jain. Vidit Jain is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 39 | |
| 3 | 5 | |
| 4 | 12 | |
| 5 | 1 | |
| 6 | 7 | |
| 7 | 114 | |
| 8 | 85 | |
| 9 | FDDB: A benchmark for face detection in unconstrained settingsbreakdown → | 558 |
| 10 | 13 | |
| 11 | 224 | |
| 12 | 24 | |
| 13 | 10 |
About Vidit Jain
Vidit Jain is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Statistical and Nonlinear Physics, having authored 13 papers that have together received 1.1k indexed citations. Recurring topics across this work include Advanced Image and Video Retrieval Techniques (6 papers), Text and Document Classification Technologies (4 papers) and Topic Modeling (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (934 citations), Signal Processing (199 citations) and Computational Mathematics (5 citations). Vidit Jain has collaborated with scholars based in United States, India and United Kingdom. Frequent co-authors include Erik Learned-Miller, Gary B. Huang, Manik Varma, Rohit Kumar Kaliyar, Andrew McCallum, Anurag Goswami, Pratik Narang, Yashvardhan Sharma, Amit Singhal and Jiebo Luo. Their work appears in journals such as Neural Computing and Applications, Proceedings of the VLDB Endowment and Lecture notes in computer science.
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.