Tarun Kalluri
- Computer Vision and Pattern Recognition top 10%
- Artificial Intelligence
- Materials Chemistry
- Computational Theory and Mathematics
- Media Technology
- Co-authors
- Manmohan ChandrakerGirish VarmaC. V. JawaharDeepak PathakDu TranShampa RaghunathanPrabhakar BhimalapuramU. Deva Priyakumar
- Topics
- Multimodal Machine Learning Applications (3 papers)Advanced Neural Network Applications (3 papers)Domain Adaptation and Few-Shot Learning (3 papers)
- Journals
- The Journal of Physical Chemistry AMachine Vision and Applications2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
- Partner nations
- United StatesIndiaDominican Republic
In The Last Decade
Tarun Kalluri
7 papers receiving 165 citations
Peers
Comparison fields: 5 of 53
- Computer Vision and Pattern Recognition 100
- Artificial Intelligence 53
- Materials Chemistry 39
- Computational Theory and Mathematics 23
- Media Technology 20
Countries citing papers authored by Tarun Kalluri
This map shows the geographic impact of Tarun Kalluri'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 Tarun Kalluri with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tarun Kalluri more than expected).
Fields of papers citing papers by Tarun Kalluri
This network shows the impact of papers produced by Tarun Kalluri. 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 Tarun Kalluri. The network helps show where Tarun Kalluri may publish in the future.
Co-authorship network of co-authors of Tarun Kalluri
This figure shows the co-authorship network connecting the top 25 collaborators of Tarun Kalluri. A scholar is included among the top collaborators of Tarun Kalluri 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 Tarun Kalluri. Tarun Kalluri 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 | 1 | |
| 4 | 42 | |
| 5 | 4 | |
| 6 | 8 | |
| 7 | 44 | |
| 8 | 70 |
About Tarun Kalluri
Tarun Kalluri is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Computational Theory and Mathematics, having authored 8 papers that have together received 170 indexed citations. Recurring topics across this work include Multimodal Machine Learning Applications (3 papers), Advanced Neural Network Applications (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 (100 citations), Media Technology (20 citations) and Artificial Intelligence (53 citations). Tarun Kalluri has collaborated with scholars based in United States, India and Dominican Republic. Frequent co-authors include Manmohan Chandraker, Girish Varma, C. V. Jawahar, Deepak Pathak, Du Tran, Shampa Raghunathan, Prabhakar Bhimalapuram, U. Deva Priyakumar, Wang‐Dong Xu and Heng Wang. Their work appears in journals such as The Journal of Physical Chemistry A, Machine Vision and Applications and 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
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.