Yash Goyal
- Computer Vision and Pattern Recognition top 1%
- Artificial Intelligence top 1%
- Information Systems
- Signal Processing
- Cognitive Neuroscience
- Co-authors
- Dhruv BatraTejas KhotDouglas Summers-StayDevi ParikhAishwarya AgrawalHarsh AgrawalStanislaw AntolKevin Kochersberger
- Topics
- Multimodal Machine Learning Applications (5 papers)Advanced Image and Video Retrieval Techniques (3 papers)Human Pose and Action Recognition (3 papers)
- Cited by
- Computer Vision and Pattern RecognitionArtificial IntelligenceHealth Information Management
- Journals
- SHILAP Revista de lepidopterologíaBMC BioinformaticsComputer Vision and Image Understanding
- Partner nations
- IndiaUnited StatesCanada
In The Last Decade
Yash Goyal
11 papers receiving 1.1k citations
Hit Papers
Peers
Comparison fields: 5 of 79
- Computer Vision and Pattern Recognition 1.0k
- Artificial Intelligence 898
- Information Systems 16
- Signal Processing 13
- Cognitive Neuroscience 12
Countries citing papers authored by Yash Goyal
This map shows the geographic impact of Yash Goyal'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 Goyal with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yash Goyal more than expected).
Fields of papers citing papers by Yash Goyal
This network shows the impact of papers produced by Yash Goyal. 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 Goyal. The network helps show where Yash Goyal may publish in the future.
Co-authorship network of co-authors of Yash Goyal
This figure shows the co-authorship network connecting the top 25 collaborators of Yash Goyal. A scholar is included among the top collaborators of Yash Goyal 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 Goyal. Yash Goyal 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 | 5 | |
| 3 | 7 | |
| 4 | 8 | |
| 5 | 13 | |
| 6 | 1 | |
| 7 | 3 | |
| 8 | 13 | |
| 9 | Predicting The Strength Enhancement Of Subgrade Soil Reinforced With Geotextile Using Artificial Neural Network And M5P Model Tree | 0 |
| 10 | Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answeringbreakdown → | 1115 |
| 11 | 9 | |
| 12 | 11 | |
| 13 | 2 |
About Yash Goyal
Yash Goyal is a scholar working on Computer Vision and Pattern Recognition, Health Information Management and Artificial Intelligence, having authored 13 papers that have together received 1.2k indexed citations. Recurring topics across this work include Multimodal Machine Learning Applications (5 papers), Advanced Image and Video Retrieval Techniques (3 papers) and Human Pose and Action Recognition (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (1.0k citations), Artificial Intelligence (898 citations) and Health Information Management (10 citations). Yash Goyal has collaborated with scholars based in India, United States and Canada. Frequent co-authors include Dhruv Batra, Tejas Khot, Douglas Summers-Stay, Devi Parikh, Aishwarya Agrawal, Harsh Agrawal, Stanislaw Antol, Kevin Kochersberger, Pooja Arora and Baljeet Kaur. Their work appears in journals such as SHILAP Revista de lepidopterología, BMC Bioinformatics and Computer Vision and Image Understanding.
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.