Xiaolong Jiang
- Computer Vision and Pattern Recognition top 2%
- Artificial Intelligence top 5%
- Safety, Risk, Reliability and Quality top 5%
- Media Technology top 5%
- Transportation top 10%
- Co-authors
- Xianbin CaoBaochang ZhangXiantong ZhenZehao XiaoDavid DoermannLing ShaoYao HuHaochen Wang
- Topics
- Advanced Neural Network Applications (11 papers)Video Surveillance and Tracking Methods (10 papers)Multimodal Machine Learning Applications (7 papers)
- Cited by
- Computer Vision and Pattern RecognitionSafety, Risk, Reliability and QualityArtificial Intelligence
- Journals
- Chemical CommunicationsIEEE Transactions on Geoscience and Remote SensingInternational Journal of Computer Vision
- Partner nations
- ChinaUnited StatesUnited Arab Emirates
In The Last Decade
Xiaolong Jiang
28 papers receiving 649 citations
Peers
Comparison fields: 5 of 84
- Computer Vision and Pattern Recognition 522
- Artificial Intelligence 264
- Safety, Risk, Reliability and Quality 78
- Media Technology 68
- Transportation 43
Countries citing papers authored by Xiaolong Jiang
This map shows the geographic impact of Xiaolong Jiang'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 Xiaolong Jiang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xiaolong Jiang more than expected).
Fields of papers citing papers by Xiaolong Jiang
This network shows the impact of papers produced by Xiaolong Jiang. 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 Xiaolong Jiang. The network helps show where Xiaolong Jiang may publish in the future.
Co-authorship network of co-authors of Xiaolong Jiang
This figure shows the co-authorship network connecting the top 25 collaborators of Xiaolong Jiang. A scholar is included among the top collaborators of Xiaolong Jiang 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 Xiaolong Jiang. Xiaolong Jiang 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 | 1 | |
| 3 | 10 | |
| 4 | 1 | |
| 5 | 2 | |
| 6 | 1 | |
| 7 | 6 | |
| 8 | 1 | |
| 9 | 39 | |
| 10 | 18 | |
| 11 | 29 | |
| 12 | 13 | |
| 13 | 3 | |
| 14 | 4 | |
| 15 | 261 | |
| 16 | 16 | |
| 17 | 2 | |
| 18 | Deep Collaborative Tracking Networks. | 4 |
| 19 | 10 | |
| 20 | 6 |
About Xiaolong Jiang
Xiaolong Jiang is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Industrial and Manufacturing Engineering, having authored 30 papers that have together received 655 indexed citations. Recurring topics across this work include Advanced Neural Network Applications (11 papers), Video Surveillance and Tracking Methods (10 papers) and Multimodal Machine Learning Applications (7 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (522 citations), Safety, Risk, Reliability and Quality (78 citations) and Artificial Intelligence (264 citations). Xiaolong Jiang has collaborated with scholars based in China, United States and United Arab Emirates. Frequent co-authors include Xianbin Cao, Baochang Zhang, Xiantong Zhen, Zehao Xiao, David Doermann, Ling Shao, Yao Hu, Haochen Wang, Song Bai and Haibing Ren. Their work appears in journals such as Chemical Communications, IEEE Transactions on Geoscience and Remote Sensing and International Journal of Computer Vision.
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.