Raghuraman Gopalan
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- Human Pose and Action Recognition 5
- Advanced Image and Video Retrieval Techniques 4
- Multimodal Machine Learning Applications 4
- Image Retrieval and Classification Techniques 3
- Face and Expression Recognition 3
- Video Surveillance and Tracking Methods 2
- Artificial Intelligence top 1%
- Domain Adaptation and Few-Shot Learning 8
- Media Technology top 2%
- Automotive Engineering top 10%
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- COVID-19 diagnosis using AI 3
- Co-authors
- Rama ChellappaRuonan LiVishal M. PatelTsai Hong HongMichael ShneierSumit ChopraS. BalakrishnanDavid Jacobs
- Journals
- IEEE Transactions on Pattern Analysis and Machine Intelligence (2 papers)International Journal of Computer Vision (1 paper)IEEE Transactions on Intelligent Transportation Systems (1 paper)
- Partner nations
- United StatesJapan
In The Last Decade
Raghuraman Gopalan
17 papers receiving 1.7k citations
Hit Papers
Peers
Comparison fields: 5 of 108
- Computer Vision and Pattern Recognition 1.1k
- Artificial Intelligence 1.1k
- Media Technology 156
- Automotive Engineering 122
- Cancer Research 131
Countries citing papers authored by Raghuraman Gopalan
This map shows the geographic impact of Raghuraman Gopalan'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 Raghuraman Gopalan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Raghuraman Gopalan more than expected).
Fields of papers citing papers by Raghuraman Gopalan
This network shows the impact of papers produced by Raghuraman Gopalan. 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 Raghuraman Gopalan. The network helps show where Raghuraman Gopalan may publish in the future.
Co-authorship network
The 19 scholars most cited alongside Raghuraman Gopalan, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2015 | 21 | |
| 2 | 2015 | 6 | |
| 3 | 2015 | 4 | |
| 4 | Visual Domain Adaptation: A survey of recent advancesbreakdown → | 2015 | 569 |
| 5 | 2014 | 136 | |
| 6 | 2014 | 21 | |
| 7 | DLID: Deep learning for domain adaptation by interpolating between domains | 2013 | 67 |
| 8 | 2013 | 4 | |
| 9 | 2013 | 4 | |
| 10 | 2013 | 4 | |
| 11 | 2012 | 51 | |
| 12 | 2012 | 134 | |
| 13 | Domain adaptation for object recognition: An unsupervised approachbreakdown → | 2011 | 666 |
| 14 | 2011 | 3 | |
| 15 | 2010 | 1 | |
| 16 | 2009 | 27 | |
| 17 | 2009 | 5 |
About Raghuraman Gopalan
Raghuraman Gopalan is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Media Technology, having authored 17 papers that have together received 1.7k indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (8 papers), Human Pose and Action Recognition (5 papers), Advanced Image and Video Retrieval Techniques (4 papers), Multimodal Machine Learning Applications (4 papers), Image Retrieval and Classification Techniques (3 papers), Face and Expression Recognition (3 papers), COVID-19 diagnosis using AI (3 papers) and Video Surveillance and Tracking Methods (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (1.1k citations), Artificial Intelligence (1.1k citations) and Media Technology (156 citations). Raghuraman Gopalan has collaborated with scholars based in United States and Japan. Frequent co-authors include Rama Chellappa, Ruonan Li, Vishal M. Patel, Tsai Hong Hong, Michael Shneier, Sumit Chopra, S. Balakrishnan, David Jacobs, Rama Chellappa and Sima Taheri. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, International Journal of Computer Vision and IEEE Transactions on Intelligent Transportation 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.