Zheda Mai
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 5%
- Radiology, Nuclear Medicine and Imaging
- Information Systems
- Media Technology top 10%
- Co-authors
- Scott SannerHyunwoo KimRuiwen LiJihwan JeongJongseong JangZhibo ZhangWei‐Lun ChaoMohamed Reda Bouadjenek
- Topics
- Domain Adaptation and Few-Shot Learning (6 papers)Multimodal Machine Learning Applications (5 papers)Recommender Systems and Techniques (3 papers)
- Cited by
- Artificial IntelligenceComputer Vision and Pattern RecognitionComputer Science Applications
- Journals
- NeurocomputingInformation Processing & ManagementJournal of Visual Communication and Image Representation
- Partner nations
- CanadaUnited StatesAustralia
In The Last Decade
Zheda Mai
10 papers receiving 542 citations
Hit Papers
Peers
Comparison fields: 5 of 72
- Artificial Intelligence 428
- Computer Vision and Pattern Recognition 272
- Radiology, Nuclear Medicine and Imaging 52
- Information Systems 35
- Media Technology 29
Countries citing papers authored by Zheda Mai
This map shows the geographic impact of Zheda Mai'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 Zheda Mai with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Zheda Mai more than expected).
Fields of papers citing papers by Zheda Mai
This network shows the impact of papers produced by Zheda Mai. 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 Zheda Mai. The network helps show where Zheda Mai may publish in the future.
Co-authorship network of co-authors of Zheda Mai
This figure shows the co-authorship network connecting the top 25 collaborators of Zheda Mai. A scholar is included among the top collaborators of Zheda Mai 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 Zheda Mai. Zheda Mai is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 0 | |
| 3 | 0 | |
| 4 | 1 | |
| 5 | 0 | |
| 6 | 41 | |
| 7 | 20 | |
| 8 | 23 | |
| 9 | 22 | |
| 10 | 1 | |
| 11 | 15 | |
| 12 | 99 | |
| 13 | Online continual learning in image classification: An empirical surveybreakdown → | 229 |
| 14 | 96 | |
| 15 | 0 |
About Zheda Mai
Zheda Mai is a scholar working on Artificial Intelligence, General Social Sciences and Computer Vision and Pattern Recognition, having authored 15 papers that have together received 549 indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (6 papers), Multimodal Machine Learning Applications (5 papers) and Recommender Systems and Techniques (3 papers). The work is most often cited by research in Artificial Intelligence (428 citations), Computer Vision and Pattern Recognition (272 citations) and Computer Science Applications (18 citations). Zheda Mai has collaborated with scholars based in Canada, United States and Australia. Frequent co-authors include Scott Sanner, Hyunwoo Kim, Ruiwen Li, Jihwan Jeong, Jongseong Jang, Zhibo Zhang, Wei‐Lun Chao, Mohamed Reda Bouadjenek, Zhaolin Gao and M. Caccia. Their work appears in journals such as Neurocomputing, Information Processing & Management and Journal of Visual Communication and Image Representation.
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.