Yikai Wang
- Computer Vision and Pattern Recognition top 5%
- Cognitive Neuroscience top 10%
- Artificial Intelligence top 10%
- Aerospace Engineering
- Signal Processing top 10%
- Topics
- Advanced Neural Network Applications (10 papers)Domain Adaptation and Few-Shot Learning (4 papers)Robotics and Sensor-Based Localization (4 papers)
- Cited by
- Computer Vision and Pattern RecognitionComputer Graphics and Computer-Aided DesignCognitive Neuroscience
In The Last Decade
Yikai Wang
18 papers receiving 532 citations
Hit Papers
Peers
Comparison fields: 5 of 76
- Computer Vision and Pattern Recognition 264
- Cognitive Neuroscience 121
- Artificial Intelligence 108
- Aerospace Engineering 67
- Signal Processing 63
Countries citing papers authored by Yikai Wang
This map shows the geographic impact of Yikai 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 Yikai Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yikai Wang more than expected).
Fields of papers citing papers by Yikai Wang
This network shows the impact of papers produced by Yikai 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 Yikai Wang. The network helps show where Yikai Wang may publish in the future.
Co-authorship network of co-authors of Yikai Wang
This figure shows the co-authorship network connecting the top 25 collaborators of Yikai Wang. A scholar is included among the top collaborators of Yikai 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 Yikai Wang. Yikai 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 | 0 | |
| 2 | Text-to-3D using Gaussian Splattingbreakdown → | 45 |
| 3 | 1 | |
| 4 | 0 | |
| 5 | 18 | |
| 6 | 0 | |
| 7 | 2 | |
| 8 | 58 | |
| 9 | 1 | |
| 10 | Multimodal Token Fusion for Vision Transformersbreakdown → | 135 |
| 11 | 31 | |
| 12 | 19 | |
| 13 | 39 | |
| 14 | Explicit Connection Distillation | 3 |
| 15 | 13 | |
| 16 | 8 | |
| 17 | 37 | |
| 18 | 15 | |
| 19 | 3 | |
| 20 | 106 |
About Yikai Wang
Yikai Wang is a scholar working on Computer Vision and Pattern Recognition, Computer Graphics and Computer-Aided Design and Signal Processing, having authored 21 papers that have together received 544 indexed citations. Recurring topics across this work include Advanced Neural Network Applications (10 papers), Domain Adaptation and Few-Shot Learning (4 papers) and Robotics and Sensor-Based Localization (4 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (264 citations), Computer Graphics and Computer-Aided Design (29 citations) and Cognitive Neuroscience (121 citations). Yikai Wang has collaborated with scholars based in China, Sweden and Poland. Frequent co-authors include Fuchun Sun, Wenbing Huang, Lele Cao, Xinghao Chen, Yunhe Wang, Yue Li, Fan Yang, Sheng Gao, Zhewei Zhang and Huaping Liu. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing and IEEE Transactions on Biomedical Engineering.
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.