Yash Patel
- Computer Vision and Pattern Recognition top 10%
- Artificial Intelligence
- Plant Science
- Aerospace Engineering
- Experimental and Cognitive Psychology
- Co-authors
- Jiřı́ MatasR. ManmathaSrikar AppalarajuGiorgos ToliasLukáš PicekMilan ŠulcDániel BaráthTong Wei
- Topics
- Visual Attention and Saliency Detection (2 papers)Image and Video Quality Assessment (2 papers)Date Palm Research Studies (2 papers)
- Journals
- SHILAP Revista de lepidopterologíaFrontiers in Plant Science2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Partner nations
- CzechiaIndiaSwitzerland
In The Last Decade
Yash Patel
9 papers receiving 110 citations
Peers
Comparison fields: 5 of 45
- Computer Vision and Pattern Recognition 66
- Artificial Intelligence 31
- Plant Science 16
- Aerospace Engineering 14
- Experimental and Cognitive Psychology 12
Countries citing papers authored by Yash Patel
This map shows the geographic impact of Yash Patel'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 Patel with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yash Patel more than expected).
Fields of papers citing papers by Yash Patel
This network shows the impact of papers produced by Yash Patel. 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 Patel. The network helps show where Yash Patel may publish in the future.
Co-authorship network of co-authors of Yash Patel
This figure shows the co-authorship network connecting the top 25 collaborators of Yash Patel. A scholar is included among the top collaborators of Yash Patel 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 Patel. Yash Patel 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 | 2 | |
| 3 | 18 | |
| 4 | 0 | |
| 5 | 28 | |
| 6 | 25 | |
| 7 | 29 | |
| 8 | 1 | |
| 9 | Hierarchical Auto-Regressive Model for Image Compression Incorporating Object Saliency and a Deep Perceptual Loss. | 2 |
| 10 | 2 | |
| 11 | 16 |
About Yash Patel
Yash Patel is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Analytical Chemistry, having authored 11 papers that have together received 123 indexed citations. Recurring topics across this work include Visual Attention and Saliency Detection (2 papers), Image and Video Quality Assessment (2 papers) and Date Palm Research Studies (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (66 citations), Geology (8 citations) and Media Technology (9 citations). Yash Patel has collaborated with scholars based in Czechia, India and Switzerland. Frequent co-authors include Jiřı́ Matas, R. Manmatha, Srikar Appalaraju, Giorgos Tolias, Lukáš Picek, Milan Šulc, Dániel Baráth, Tong Wei, Alexander Shekhovtsov and David R. Miller. Their work appears in journals such as SHILAP Revista de lepidopterología, Frontiers in Plant Science and 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
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.