Yogesh Balaji
- Computer Vision and Pattern Recognition top 1%
- Artificial Intelligence top 2%
- Radiology, Nuclear Medicine and Imaging top 10%
- Cancer Research
- Media Technology top 5%
- Co-authors
- Rama ChellappaSwami SankaranarayananCarlos D. CastilloArpit JainSer Nam LimSoheil FeiziA. N. RajagopalanMartin Renqiang Min
- Topics
- Generative Adversarial Networks and Image Synthesis (6 papers)Multimodal Machine Learning Applications (6 papers)Domain Adaptation and Few-Shot Learning (6 papers)
- Journals
- 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)arXiv (Cornell University)Neural Information Processing Systems
- Partner nations
- United StatesIndiaUnited Kingdom
In The Last Decade
Yogesh Balaji
12 papers receiving 1.1k citations
Hit Papers
Peers
Comparison fields: 5 of 86
- Computer Vision and Pattern Recognition 879
- Artificial Intelligence 772
- Radiology, Nuclear Medicine and Imaging 169
- Cancer Research 76
- Media Technology 65
Countries citing papers authored by Yogesh Balaji
This map shows the geographic impact of Yogesh Balaji'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 Yogesh Balaji with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yogesh Balaji more than expected).
Fields of papers citing papers by Yogesh Balaji
This network shows the impact of papers produced by Yogesh Balaji. 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 Yogesh Balaji. The network helps show where Yogesh Balaji may publish in the future.
Co-authorship network of co-authors of Yogesh Balaji
This figure shows the co-authorship network connecting the top 25 collaborators of Yogesh Balaji. A scholar is included among the top collaborators of Yogesh Balaji 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 Yogesh Balaji. Yogesh Balaji is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 6 | |
| 2 | 60 | |
| 3 | 19 | |
| 4 | 8 | |
| 5 | 10 | |
| 6 | 26 | |
| 7 | 62 | |
| 8 | MetaReg: towards domain generalization using meta-regularization | 227 |
| 9 | Generate to Adapt: Aligning Domains Using Generative Adversarial Networksbreakdown → | 409 |
| 10 | TFGAN: Improving Conditioning for Text-to-Video Synthesis | 0 |
| 11 | 284 | |
| 12 | 41 | |
| 13 | Unsupervised Domain Adaptation for Semantic Segmentation with GANs. | 26 |
About Yogesh Balaji
Yogesh Balaji is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Media Technology, having authored 13 papers that have together received 1.2k indexed citations. Recurring topics across this work include Generative Adversarial Networks and Image Synthesis (6 papers), Multimodal Machine Learning Applications (6 papers) and Domain Adaptation and Few-Shot Learning (6 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (879 citations), Artificial Intelligence (772 citations) and Media Technology (65 citations). Yogesh Balaji has collaborated with scholars based in United States, India and United Kingdom. Frequent co-authors include Rama Chellappa, Swami Sankaranarayanan, Carlos D. Castillo, Arpit Jain, Ser Nam Lim, Soheil Feizi, A. N. Rajagopalan, Martin Renqiang Min, Hans Peter Graf and Bing Bai. Their work appears in journals such as 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), arXiv (Cornell University) and Neural Information Processing Systems.
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.