Junjiao Tian
- Computer Vision and Pattern Recognition top 5%
- Artificial Intelligence
- Automotive Engineering top 10%
- Aerospace Engineering
- Control and Systems Engineering
- Co-authors
- Zsolt KiraYen‐Cheng LiuChih‐Yao MaChia-Wen KuoHan-Pang ChiuNiluthpol Chowdhury MithunZachary SeymourJean Oh
- Topics
- Multimodal Machine Learning Applications (3 papers)Advanced Neural Network Applications (3 papers)Domain Adaptation and Few-Shot Learning (3 papers)
- Journals
- 2022 International Conference on Robotics and Automation (ICRA)2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
- Partner nations
- United States
In The Last Decade
Junjiao Tian
7 papers receiving 289 citations
Peers
Comparison fields: 5 of 42
- Computer Vision and Pattern Recognition 156
- Artificial Intelligence 69
- Automotive Engineering 63
- Aerospace Engineering 60
- Control and Systems Engineering 54
Countries citing papers authored by Junjiao Tian
This map shows the geographic impact of Junjiao Tian'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 Junjiao Tian with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Junjiao Tian more than expected).
Fields of papers citing papers by Junjiao Tian
This network shows the impact of papers produced by Junjiao Tian. 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 Junjiao Tian. The network helps show where Junjiao Tian may publish in the future.
Co-authorship network of co-authors of Junjiao Tian
This figure shows the co-authorship network connecting the top 25 collaborators of Junjiao Tian. A scholar is included among the top collaborators of Junjiao Tian 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 Junjiao Tian. Junjiao Tian is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 16 | |
| 2 | 11 | |
| 3 | Recall Loss for Imbalanced Image Classification and Semantic Segmentation | 3 |
| 4 | 139 | |
| 5 | 100 | |
| 6 | 15 | |
| 7 | 6 |
About Junjiao Tian
Junjiao Tian is a scholar working on Computer Vision and Pattern Recognition, Computer Science Applications and Artificial Intelligence, having authored 7 papers that have together received 290 indexed citations. Recurring topics across this work include Multimodal Machine Learning Applications (3 papers), Advanced Neural Network Applications (3 papers) and Domain Adaptation and Few-Shot Learning (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (156 citations), Automotive Engineering (63 citations) and Instrumentation (10 citations). Junjiao Tian has collaborated with scholars based in United States. Frequent co-authors include Zsolt Kira, Yen‐Cheng Liu, Chih‐Yao Ma, Chia-Wen Kuo, Han-Pang Chiu, Niluthpol Chowdhury Mithun, Zachary Seymour and Jean Oh. Their work appears in journals such as 2022 International Conference on Robotics and Automation (ICRA) and 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
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.