Amir Tamrakar
- Computer Vision and Pattern Recognition top 5%
- Artificial Intelligence top 10%
- Developmental and Educational Psychology
- Signal Processing
- Computer Science Applications top 10%
- Co-authors
- Ajay DivakaranBenjamin B. KimiaJingen LiuSaad AliOmar JavedHui ChengHarpreet SawhneyBehjat Siddiquie
- Topics
- Multimodal Machine Learning Applications (6 papers)Human Pose and Action Recognition (5 papers)Speech and dialogue systems (4 papers)
- Cited by
- Computer Vision and Pattern RecognitionComputer Science ApplicationsArtificial Intelligence
- Journals
- IEEE Transactions on Pattern Analysis and Machine IntelligenceInternational Journal of Computer VisionAI Communications
- Partner nations
- United States
In The Last Decade
Amir Tamrakar
20 papers receiving 321 citations
Peers
Comparison fields: 5 of 57
- Computer Vision and Pattern Recognition 254
- Artificial Intelligence 105
- Developmental and Educational Psychology 34
- Signal Processing 29
- Computer Science Applications 28
Countries citing papers authored by Amir Tamrakar
This map shows the geographic impact of Amir Tamrakar'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 Amir Tamrakar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Amir Tamrakar more than expected).
Fields of papers citing papers by Amir Tamrakar
This network shows the impact of papers produced by Amir Tamrakar. 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 Amir Tamrakar. The network helps show where Amir Tamrakar may publish in the future.
Co-authorship network of co-authors of Amir Tamrakar
This figure shows the co-authorship network connecting the top 25 collaborators of Amir Tamrakar. A scholar is included among the top collaborators of Amir Tamrakar 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 Amir Tamrakar. Amir Tamrakar 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 | 0 | |
| 3 | 7 | |
| 4 | 3 | |
| 5 | 3 | |
| 6 | 6 | |
| 7 | 2 | |
| 8 | 6 | |
| 9 | 4 | |
| 10 | 28 | |
| 11 | 34 | |
| 12 | 60 | |
| 13 | SRI-Sarnoff AURORA System at TRECVID 2012 Multimedia Event Detection and Recounting. | 14 |
| 14 | 98 | |
| 15 | Team SRI-Sarnoff 's AURORA System @ TRECVID 2011 | 3 |
| 16 | 14 | |
| 17 | 19 | |
| 18 | 2 | |
| 19 | 8 | |
| 20 | 3 |
About Amir Tamrakar
Amir Tamrakar is a scholar working on Computer Vision and Pattern Recognition, Computer Science Applications and Artificial Intelligence, having authored 22 papers that have together received 343 indexed citations. Recurring topics across this work include Multimodal Machine Learning Applications (6 papers), Human Pose and Action Recognition (5 papers) and Speech and dialogue systems (4 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (254 citations), Computer Science Applications (28 citations) and Artificial Intelligence (105 citations). Amir Tamrakar has collaborated with scholars based in United States. Frequent co-authors include Ajay Divakaran, Benjamin B. Kimia, Jingen Liu, Saad Ali, Omar Javed, Hui Cheng, Harpreet Sawhney, Behjat Siddiquie, Qian Yu and Mohamed R. Amer. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, International Journal of Computer Vision and AI Communications.
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.