Lijun Yu
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- Video Surveillance and Tracking Methods 8
- Human Pose and Action Recognition 6
- Multimodal Machine Learning Applications 4
- Generative Adversarial Networks and Image Synthesis 2
- Advanced Neural Network Applications 2
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- Autonomous Vehicle Technology and Safety 3
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- Anomaly Detection Techniques and Applications 4
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- Traffic Prediction and Management Techniques 2
- Co-authors
- Alexander G. HauptmannWenhe LiuGuoliang KangDawei ZhangXiangqun ChenJosé LezamaHan ZhangHui‐Wen Chang
- Cited by
- Computer Vision and Pattern RecognitionAutomotive EngineeringComputer Graphics and Computer-Aided Design
- Partner nations
- United StatesChinaAustralia
In The Last Decade
Lijun Yu
21 papers receiving 262 citations
Peers
Comparison fields: 5 of 79
- Computer Vision and Pattern Recognition 178
- Automotive Engineering 47
- Computer Graphics and Computer-Aided Design 8
- Artificial Intelligence 64
- Transportation 13
Countries citing papers authored by Lijun Yu
This map shows the geographic impact of Lijun Yu'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 Lijun Yu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lijun Yu more than expected).
Fields of papers citing papers by Lijun Yu
This network shows the impact of papers produced by Lijun Yu. 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 Lijun Yu. The network helps show where Lijun Yu may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Lijun Yu, 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 | 2023 | 2 | |
| 2 | 2023 | 30 | |
| 3 | 2022 | 13 | |
| 4 | 2022 | 4 | |
| 5 | 2022 | 2 | |
| 6 | 2022 | 7 | |
| 7 | CMU Informedia at TRECVID 2020: Activity Detection with Dense Spatio-temporal Proposals. | 2020 | 3 |
| 8 | Antioxidant activity and potential ameliorating effective ingredients for high altitude-induced fatigue from Gansu Maxianhao (Pedicularis Kansuensis Maxim.). | 2020 | 8 |
| 9 | 2020 | 20 | |
| 10 | 2020 | 50 | |
| 11 | 2020 | 9 | |
| 12 | 2020 | 29 | |
| 13 | 2019 | 1 | |
| 14 | MMVG-INF-Etrol@TRECVID 2019: Activities in Extended Video. | 2019 | 8 |
| 15 | 2019 | 11 | |
| 16 | 2018 | 20 | |
| 17 | 2017 | 2 | |
| 18 | 2016 | 2 | |
| 19 | 2015 | 3 | |
| 20 | 2012 | 3 |
About Lijun Yu
Lijun Yu is a scholar working on Computer Vision and Pattern Recognition, Software and Automotive Engineering, having authored 21 papers that have together received 272 indexed citations. Recurring topics across this work include Video Surveillance and Tracking Methods (8 papers), Human Pose and Action Recognition (6 papers), Multimodal Machine Learning Applications (4 papers), Anomaly Detection Techniques and Applications (4 papers), Autonomous Vehicle Technology and Safety (3 papers), Generative Adversarial Networks and Image Synthesis (2 papers), Advanced Neural Network Applications (2 papers) and Traffic Prediction and Management Techniques (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (178 citations), Automotive Engineering (47 citations) and Computer Graphics and Computer-Aided Design (8 citations). Lijun Yu has collaborated with scholars based in United States, China and Australia. Frequent co-authors include Alexander G. Hauptmann, Wenhe Liu, Guoliang Kang, Dawei Zhang, Xiangqun Chen, José Lezama, Han Zhang, Alexander G. Hauptmann, Hui‐Wen Chang and Kihyuk Sohn. Their work appears in journals such as Cell and Tissue Research, Psychology of Music, PLoS ONE, Cell Communication and Signaling and TRECVID.
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.