Yu-Xiong Wang
About
In The Last Decade
Yu-Xiong Wang
79 papers receiving 3.7k citations
Hit Papers
Peers
Comparison fields: 5 of 161
- Computer Vision and Pattern Recognition 1.6k
- Artificial Intelligence 1.4k
- Renewable Energy, Sustainability and the Environment 1.0k
- Materials Chemistry 923
- Electrical and Electronic Engineering 529
Countries citing papers authored by Yu-Xiong Wang
This map shows the geographic impact of Yu-Xiong 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 Yu-Xiong Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yu-Xiong Wang more than expected).
Fields of papers citing papers by Yu-Xiong Wang
This network shows the impact of papers produced by Yu-Xiong 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 Yu-Xiong Wang. The network helps show where Yu-Xiong Wang may publish in the future.
Co-authorship network of co-authors of Yu-Xiong Wang
This figure shows the co-authorship network connecting the top 25 collaborators of Yu-Xiong Wang. A scholar is included among the top collaborators of Yu-Xiong 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 Yu-Xiong Wang. Yu-Xiong 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 | 1 | |
| 2 | 0 | |
| 3 | 1 | |
| 4 | 0 | |
| 5 | 1 | |
| 6 | 1 | |
| 7 | 29 | |
| 8 | 15 | |
| 9 | 3 | |
| 10 | 1 | |
| 11 | 12 | |
| 12 | 28 | |
| 13 | 10 | |
| 14 | 77 | |
| 15 | Image-Level or Object-Level? A Tale of Two Resampling Strategies for Long-Tailed Detection | 5 |
| 16 | 4 | |
| 17 | 25 | |
| 18 | Learning to Model the Tail | 93 |
| 19 | Learning from small sample sets by combining unsupervised meta-training with CNNs | 40 |
| 20 | 19 |
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.