Piyush Sharma
- Computer Vision and Pattern Recognition top 1%
- Artificial Intelligence top 2%
- Sociology and Political Science
- Language and Linguistics
- Signal Processing
- Co-authors
- Radu SoricutSebastian GoodmanNan DingMaxwell ForbesSerge BelongieSoravit ChangpinyoArjun AkulaSong‐Chun Zhu
- Topics
- Multimodal Machine Learning Applications (6 papers)Advanced Image and Video Retrieval Techniques (3 papers)Domain Adaptation and Few-Shot Learning (3 papers)
- Journals
- arXiv (Cornell University)Lirias (KU Leuven)Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
- Partner nations
- United StatesIndiaChina
In The Last Decade
Piyush Sharma
8 papers receiving 1.0k citations
Hit Papers
Peers
Comparison fields: 5 of 67
- Computer Vision and Pattern Recognition 907
- Artificial Intelligence 707
- Sociology and Political Science 26
- Language and Linguistics 18
- Signal Processing 17
Countries citing papers authored by Piyush Sharma
This map shows the geographic impact of Piyush Sharma'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 Piyush Sharma with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Piyush Sharma more than expected).
Fields of papers citing papers by Piyush Sharma
This network shows the impact of papers produced by Piyush Sharma. 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 Piyush Sharma. The network helps show where Piyush Sharma may publish in the future.
Co-authorship network of co-authors of Piyush Sharma
This figure shows the co-authorship network connecting the top 25 collaborators of Piyush Sharma. A scholar is included among the top collaborators of Piyush Sharma 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 Piyush Sharma. Piyush Sharma 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 | 0 | |
| 4 | 13 | |
| 5 | 1 | |
| 6 | Weakly Supervised Content Selection for Improved Image Captioning | 1 |
| 7 | 23 | |
| 8 | 7 | |
| 9 | Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioningbreakdown → | 1004 |
| 10 | 2 |
About Piyush Sharma
Piyush Sharma is a scholar working on Computer Vision and Pattern Recognition, Hardware and Architecture and Artificial Intelligence, having authored 10 papers that have together received 1.1k indexed citations. Recurring topics across this work include Multimodal Machine Learning Applications (6 papers), Advanced Image and Video Retrieval Techniques (3 papers) and Domain Adaptation and Few-Shot Learning (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (907 citations), Artificial Intelligence (707 citations) and Language and Linguistics (18 citations). Piyush Sharma has collaborated with scholars based in United States, India and China. Frequent co-authors include Radu Soricut, Sebastian Goodman, Nan Ding, Maxwell Forbes, Serge Belongie, Soravit Changpinyo, Arjun Akula, Song‐Chun Zhu, Boqing Gong and Bo Pang. Their work appears in journals such as arXiv (Cornell University), Lirias (KU Leuven) and Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing.
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.