Zifeng Wang
- Artificial Intelligence top 2%
- Computer Vision and Pattern Recognition top 2%
- Electrical and Electronic Engineering
- Radiology, Nuclear Medicine and Imaging top 10%
- Signal Processing top 10%
- Co-authors
- Jennifer DyJimeng SunZhenbang WuD.C. AgarwalStratis IoannidisKaushik ChowdhuryTong JianTomas Pfister
- Topics
- Domain Adaptation and Few-Shot Learning (6 papers)Topic Modeling (5 papers)Wireless Signal Modulation Classification (5 papers)
- Partner nations
- United StatesChinaUnited Kingdom
In The Last Decade
Zifeng Wang
29 papers receiving 1000 citations
Hit Papers
Peers
Comparison fields: 5 of 96
- Artificial Intelligence 701
- Computer Vision and Pattern Recognition 382
- Electrical and Electronic Engineering 162
- Radiology, Nuclear Medicine and Imaging 140
- Signal Processing 100
Countries citing papers authored by Zifeng Wang
This map shows the geographic impact of Zifeng Wang'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 Zifeng Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Zifeng Wang more than expected).
Fields of papers citing papers by Zifeng Wang
This network shows the impact of papers produced by Zifeng Wang. 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 Zifeng Wang. The network helps show where Zifeng Wang may publish in the future.
Co-authorship network of co-authors of Zifeng Wang
This figure shows the co-authorship network connecting the top 25 collaborators of Zifeng Wang. A scholar is included among the top collaborators of Zifeng Wang 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 Zifeng Wang. Zifeng Wang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 8 | |
| 2 | 0 | |
| 3 | 7 | |
| 4 | 3 | |
| 5 | 12 | |
| 6 | 15 | |
| 7 | MedCLIP: Contrastive Learning from Unpaired Medical Images and Textbreakdown → | 235 |
| 8 | Learning to Prompt for Continual Learningbreakdown → | 329 |
| 9 | 3 | |
| 10 | 5 | |
| 11 | 42 | |
| 12 | Information Theoretic Counterfactual Learning from Missing-Not-At-Random Feedback | 1 |
| 13 | Instance-wise Feature Grouping | 1 |
| 14 | 171 | |
| 15 | 16 | |
| 16 | 6 | |
| 17 | 4 | |
| 18 | 6 | |
| 19 | 2 | |
| 20 | 6 |
About Zifeng Wang
Zifeng Wang is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and General Social Sciences, having authored 30 papers that have together received 1.0k indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (6 papers), Topic Modeling (5 papers) and Wireless Signal Modulation Classification (5 papers). The work is most often cited by research in Health Informatics (34 citations), Artificial Intelligence (701 citations) and Computer Vision and Pattern Recognition (382 citations). Zifeng Wang has collaborated with scholars based in United States, China and United Kingdom. Frequent co-authors include Jennifer Dy, Jimeng Sun, Zhenbang Wu, D.C. Agarwal, Stratis Ioannidis, Kaushik Chowdhury, Tong Jian, Tomas Pfister, Xiaoqi Ren and Han Zhang. Their work appears in journals such as ACM Computing Surveys, IEEE Transactions on Vehicular Technology and PLoS Computational Biology.
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.