Junjie Yang
- Computer Vision and Pattern Recognition top 10%
- Artificial Intelligence
- Media Technology top 10%
- Biomedical Engineering
- Analytical Chemistry
- Co-authors
- Yongqing YangKezhu TanTao WuShuwen WangYong YangYingbin LiangYao LiuLiqiang Yan
- Topics
- Advanced Image Fusion Techniques (4 papers)Image and Signal Denoising Methods (4 papers)Advanced Vision and Imaging (4 papers)
- Partner nations
- ChinaUnited StatesTaiwan
In The Last Decade
Junjie Yang
38 papers receiving 186 citations
Peers
Comparison fields: 5 of 79
- Computer Vision and Pattern Recognition 75
- Artificial Intelligence 53
- Media Technology 32
- Biomedical Engineering 22
- Analytical Chemistry 21
Countries citing papers authored by Junjie Yang
This map shows the geographic impact of Junjie Yang'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 Junjie Yang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Junjie Yang more than expected).
Fields of papers citing papers by Junjie Yang
This network shows the impact of papers produced by Junjie Yang. 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 Junjie Yang. The network helps show where Junjie Yang may publish in the future.
Co-authorship network of co-authors of Junjie Yang
This figure shows the co-authorship network connecting the top 25 collaborators of Junjie Yang. A scholar is included among the top collaborators of Junjie Yang 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 Junjie Yang. Junjie Yang 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 | 1 | |
| 3 | 1 | |
| 4 | 1 | |
| 5 | 2 | |
| 6 | 1 | |
| 7 | 1 | |
| 8 | 1 | |
| 9 | 1 | |
| 10 | 1 | |
| 11 | 2 | |
| 12 | 26 | |
| 13 | 1 | |
| 14 | Provably Faster Algorithms for Bilevel Optimization and Applications to Meta-Learning | 5 |
| 15 | 6 | |
| 16 | 2 | |
| 17 | Multi-Step Model-Agnostic Meta-Learning: Convergence and Improved Algorithms. | 4 |
| 18 | Deepening Hidden Representations from Pre-trained Language Models for Natural Language Understanding. | 2 |
| 19 | SGD Converges to Global Minimum in Deep Learning via Star-convex Path. | 6 |
| 20 | 1 |
About Junjie Yang
Junjie Yang is a scholar working on Media Technology, Computer Vision and Pattern Recognition and Developmental Biology, having authored 40 papers that have together received 194 indexed citations. Recurring topics across this work include Advanced Image Fusion Techniques (4 papers), Image and Signal Denoising Methods (4 papers) and Advanced Vision and Imaging (4 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (75 citations), Media Technology (32 citations) and Analytical Chemistry (21 citations). Junjie Yang has collaborated with scholars based in China, United States and Taiwan. Frequent co-authors include Yongqing Yang, Kezhu Tan, Tao Wu, Shuwen Wang, Yong Yang, Yingbin Liang, Yao Liu, Liqiang Yan, Liguo Wang and Weixiao Wang. Their work appears in journals such as PLoS ONE, Scientific Reports and IEEE Access.
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.