Soyeb Nagori
- Computer Vision and Pattern Recognition top 5%
- Artificial Intelligence
- Automotive Engineering
- Biomedical Engineering
- Human-Computer Interaction top 10%
- Co-authors
- Manu MathewDeepak PoddarDebapriya MajiMihir ModyHrushikesh GarudZoran NikolićGirish VarmaC. V. Jawahar
- Topics
- Autonomous Vehicle Technology and Safety (5 papers)Advanced Neural Network Applications (4 papers)CCD and CMOS Imaging Sensors (3 papers)
- Journals
- 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)Electronic Imaging
- Partner nations
- United StatesIndia
In The Last Decade
Soyeb Nagori
14 papers receiving 290 citations
Hit Papers
Peers
Comparison fields: 5 of 71
- Computer Vision and Pattern Recognition 185
- Artificial Intelligence 48
- Automotive Engineering 46
- Biomedical Engineering 46
- Human-Computer Interaction 31
Countries citing papers authored by Soyeb Nagori
This map shows the geographic impact of Soyeb Nagori'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 Soyeb Nagori with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Soyeb Nagori more than expected).
Fields of papers citing papers by Soyeb Nagori
This network shows the impact of papers produced by Soyeb Nagori. 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 Soyeb Nagori. The network helps show where Soyeb Nagori may publish in the future.
Co-authorship network of co-authors of Soyeb Nagori
This figure shows the co-authorship network connecting the top 25 collaborators of Soyeb Nagori. A scholar is included among the top collaborators of Soyeb Nagori 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 Soyeb Nagori. Soyeb Nagori 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 | YOLO-Pose: Enhancing YOLO for Multi Person Pose Estimation Using Object Keypoint Similarity Lossbreakdown → | 192 |
| 3 | 4 | |
| 4 | 15 | |
| 5 | 4 | |
| 6 | 4 | |
| 7 | 6 | |
| 8 | 23 | |
| 9 | 1 | |
| 10 | 14 | |
| 11 | 13 | |
| 12 | 13 | |
| 13 | 3 | |
| 14 | 12 | |
| 15 | 0 |
About Soyeb Nagori
Soyeb Nagori is a scholar working on Computer Vision and Pattern Recognition, Automotive Engineering and Media Technology, having authored 15 papers that have together received 305 indexed citations. Recurring topics across this work include Autonomous Vehicle Technology and Safety (5 papers), Advanced Neural Network Applications (4 papers) and CCD and CMOS Imaging Sensors (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (185 citations), Human-Computer Interaction (31 citations) and Automotive Engineering (46 citations). Soyeb Nagori has collaborated with scholars based in United States and India. Frequent co-authors include Manu Mathew, Deepak Poddar, Debapriya Maji, Mihir Mody, Hrushikesh Garud, Zoran Nikolić, Girish Varma and C. V. Jawahar. Their work appears in journals such as 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) and Electronic Imaging.
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.