Yue Cao
- Computer Vision and Pattern Recognition top 0.02%
- Artificial Intelligence top 0.05%
- Media Technology top 0.05%
- Radiology, Nuclear Medicine and Imaging top 0.5%
- Aerospace Engineering top 0.5%
- Topics
- Advanced Neural Network Applications (14 papers)Domain Adaptation and Few-Shot Learning (14 papers)Multimodal Machine Learning Applications (7 papers)
- Journals
- IEEE Transactions on Pattern Analysis and Machine IntelligenceSensorsJournal of Chemical Theory and Computation
- Partner nations
- ChinaUnited StatesCanada
In The Last Decade
Yue Cao
26 papers receiving 21.6k citations
Hit Papers
Peers
Comparison fields: 5 of 198
- Computer Vision and Pattern Recognition 13.2k
- Artificial Intelligence 5.9k
- Media Technology 3.5k
- Radiology, Nuclear Medicine and Imaging 2.2k
- Aerospace Engineering 1.8k
Countries citing papers authored by Yue Cao
This map shows the geographic impact of Yue Cao'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 Yue Cao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yue Cao more than expected).
Fields of papers citing papers by Yue Cao
This network shows the impact of papers produced by Yue Cao. 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 Yue Cao. The network helps show where Yue Cao may publish in the future.
Co-authorship network of co-authors of Yue Cao
This figure shows the co-authorship network connecting the top 25 collaborators of Yue Cao. A scholar is included among the top collaborators of Yue Cao 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 Yue Cao. Yue Cao 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 | 8 | |
| 3 | 1 | |
| 4 | 63 | |
| 5 | 3 | |
| 6 | Video Swin Transformerbreakdown → | 1129 |
| 7 | 20 | |
| 8 | Swin Transformer: Hierarchical Vision Transformer using Shifted Windowsbreakdown → | 17584 |
| 9 | 74 | |
| 10 | 3 | |
| 11 | VL-BERT: Pre-training of Generic Visual-Linguistic Representations | 112 |
| 12 | 13 | |
| 13 | 32 | |
| 14 | 217 | |
| 15 | 10 | |
| 16 | 9 | |
| 17 | 76 | |
| 18 | 91 | |
| 19 | 0 | |
| 20 | 131 |
About Yue Cao
Yue Cao is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Media Technology, having authored 28 papers that have together received 22.0k indexed citations. Recurring topics across this work include Advanced Neural Network Applications (14 papers), Domain Adaptation and Few-Shot Learning (14 papers) and Multimodal Machine Learning Applications (7 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (13.2k citations), Media Technology (3.5k citations) and Artificial Intelligence (5.9k citations). Yue Cao has collaborated with scholars based in China, United States and Canada. Frequent co-authors include Han Hu, Zheng Zhang, Yixuan Wei, Ze Liu, Yutong Lin, Stephen Lin, Baining Guo, Ning Jia, Zhenda Xie and Zhuliang Yao. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Sensors and Journal of Chemical Theory and Computation.
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.