Yanglan Ou
Impact in
- Automotive Engineering top 5%
- Autonomous Vehicle Technology and Safety
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- Traffic and Road Safety
Papers in
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- Medical Image Segmentation Techniques 2
- Video Surveillance and Tracking Methods 1
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- Sentiment Analysis and Opinion Mining 1
- Advanced Text Analysis Techniques 1
- Co-authors
- Ye Yuan (3 shared papers)Xinshuo Weng (1 shared paper)Kris Kitani (1 shared paper)Kathleen M. Carley (1 shared paper)Binxuan Huang (1 shared paper)Kelvin Wong (2 shared papers)Stephen T.C. Wong (2 shared papers)John Volpi (2 shared papers)
- Journals
- Lecture notes in computer science (3 papers)2021 IEEE/CVF International Conference on Computer Vision (ICCV) (1 paper)
- Partner nations
- United StatesIsrael
In The Last Decade
Yanglan Ou
5 papers receiving 674 citations
Yanglan Ou's Hit Papers
Peers
Comparison fields: 5 of 48
- Automotive Engineering 296
- Safety, Risk, Reliability and Quality 115
- Artificial Intelligence 402
- Computer Vision and Pattern Recognition 209
- Building and Construction 104
Countries citing papers authored by Yanglan Ou
This map shows the geographic impact of Yanglan Ou'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 Yanglan Ou with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yanglan Ou more than expected).
Fields of papers citing papers by Yanglan Ou
This network shows the impact of papers produced by Yanglan Ou. 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 Yanglan Ou. The network helps show where Yanglan Ou may publish in the future.
Co-authors
The 15 scholars most cited alongside Yanglan Ou, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | AgentFormer: Agent-Aware Transformers for Socio-Temporal Multi-Agent Forecasting Hit paper breakdown → | 2021 | 387 |
| 2 | 2018 | 269 | |
| 3 | 2022 | 17 | |
| 4 | 2021 | 13 | |
| 5 | 2020 | 7 |
About Yanglan Ou
Yanglan Ou is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Automotive Engineering, Epidemiology and Radiology, Nuclear Medicine and Imaging, having authored 5 papers that have together received 693 indexed citations. Recurring topics across this work include Medical Image Segmentation Techniques (2 papers), Sentiment Analysis and Opinion Mining (1 paper), MRI in cancer diagnosis (1 paper), Brain Tumor Detection and Classification (1 paper), Autonomous Vehicle Technology and Safety (1 paper), Advanced Neuroimaging Techniques and Applications (1 paper), Advanced Text Analysis Techniques (1 paper) and Video Surveillance and Tracking Methods (1 paper). The work is most often cited by research in Automotive Engineering (296 citations), Safety, Risk, Reliability and Quality (115 citations), Artificial Intelligence (402 citations), Computer Vision and Pattern Recognition (209 citations) and Building and Construction (104 citations). Yanglan Ou has collaborated with scholars based in United States and Israel. Frequent co-authors include Ye Yuan, Xinshuo Weng, Kris Kitani, Kathleen M. Carley, Binxuan Huang, Kelvin Wong, Stephen T.C. Wong, John Volpi, James Z. Wang and Xiaolei Huang. Their work appears in journals such as Lecture notes in computer science and 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
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.