Yash Bhalgat
- Computer Vision and Pattern Recognition top 10%
- Artificial Intelligence
- Electrical and Electronic Engineering
- Aerospace Engineering
- Control and Systems Engineering
- Co-authors
- Markus NagelNojun KwakTijmen BlankevoortJinwon LeeHannah Rose KirkMax BainYifu TaoNived Chebrolu
- Topics
- Advanced Neural Network Applications (3 papers)Robotics and Sensor-Based Localization (3 papers)Advanced Image and Video Retrieval Techniques (2 papers)
- Journals
- Seoul National University Open Repository (Seoul National University)2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)Neural Information Processing Systems
- Partner nations
- United KingdomSouth KoreaCanada
In The Last Decade
Yash Bhalgat
7 papers receiving 132 citations
Peers
Comparison fields: 5 of 38
- Computer Vision and Pattern Recognition 100
- Artificial Intelligence 72
- Electrical and Electronic Engineering 19
- Aerospace Engineering 18
- Control and Systems Engineering 9
Countries citing papers authored by Yash Bhalgat
This map shows the geographic impact of Yash Bhalgat'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 Yash Bhalgat with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yash Bhalgat more than expected).
Fields of papers citing papers by Yash Bhalgat
This network shows the impact of papers produced by Yash Bhalgat. 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 Yash Bhalgat. The network helps show where Yash Bhalgat may publish in the future.
Co-authorship network of co-authors of Yash Bhalgat
This figure shows the co-authorship network connecting the top 25 collaborators of Yash Bhalgat. A scholar is included among the top collaborators of Yash Bhalgat 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 Yash Bhalgat. Yash Bhalgat 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 | 11 | |
| 3 | 3 | |
| 4 | 9 | |
| 5 | 3 | |
| 6 | Structured Convolutions for Efficient Neural Network Design | 1 |
| 7 | 104 | |
| 8 | 2 |
About Yash Bhalgat
Yash Bhalgat is a scholar working on Computer Vision and Pattern Recognition, Instrumentation and Human-Computer Interaction, having authored 8 papers that have together received 139 indexed citations. Recurring topics across this work include Advanced Neural Network Applications (3 papers), Robotics and Sensor-Based Localization (3 papers) and Advanced Image and Video Retrieval Techniques (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (100 citations), Artificial Intelligence (72 citations) and Media Technology (7 citations). Yash Bhalgat has collaborated with scholars based in United Kingdom, South Korea and Canada. Frequent co-authors include Markus Nagel, Nojun Kwak, Tijmen Blankevoort, Jinwon Lee, Hannah Rose Kirk, Max Bain, Yifu Tao, Nived Chebrolu, Maurice Fallon and João F. Henriques. Their work appears in journals such as Seoul National University Open Repository (Seoul National University), 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 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.