Gang Yu
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- Human Pose and Action Recognition 17
- Advanced Neural Network Applications 15
- Multimodal Machine Learning Applications 15
- Video Surveillance and Tracking Methods 13
- Advanced Image and Video Retrieval Techniques 11
- Video Analysis and Summarization 6
- Media Technology top 0.2%
- Artificial Intelligence top 0.5%
- Domain Adaptation and Few-Shot Learning 10
- Human-Computer Interaction top 2%
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- 3D Shape Modeling and Analysis 6
- Journals
- SHILAP Revista de lepidopterología (1 paper)The Journal of Cell Biology (1 paper)IEEE Transactions on Pattern Analysis and Machine Intelligence (1 paper)
- Partner nations
- ChinaUnited StatesSingapore
In The Last Decade
Gang Yu
98 papers receiving 6.0k citations
Hit Papers
Peers
Comparison fields: 5 of 170
- Computer Vision and Pattern Recognition 4.8k
- Media Technology 1.0k
- Artificial Intelligence 1.4k
- Safety, Risk, Reliability and Quality 234
- Human-Computer Interaction 141
Countries citing papers authored by Gang Yu
This map shows the geographic impact of Gang 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 Gang Yu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gang Yu more than expected).
Fields of papers citing papers by Gang Yu
This network shows the impact of papers produced by Gang 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 Gang Yu. The network helps show where Gang Yu may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Gang 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 | 2025 | 1 | |
| 2 | 2025 | 0 | |
| 3 | 2024 | 1 | |
| 4 | 2024 | 1 | |
| 5 | 2024 | 4 | |
| 6 | 2024 | 7 | |
| 7 | 2024 | 43 | |
| 8 | 2024 | 14 | |
| 9 | 2023 | 17 | |
| 10 | 2023 | 14 | |
| 11 | 2023 | 2 | |
| 12 | 2023 | 3 | |
| 13 | 2023 | 1 | |
| 14 | 2023 | 20 | |
| 15 | 2022 | 22 | |
| 16 | Context Prior for Scene Segmentationbreakdown → | 2020 | 209 |
| 17 | 2019 | 203 | |
| 18 | Objects365: A Large-Scale, High-Quality Dataset for Object Detectionbreakdown → | 2019 | 373 |
| 19 | Learning a Discriminative Feature Network for Semantic Segmentationbreakdown → | 2018 | 605 |
| 20 | [Absorption and distribution of a hematoporphyrin derivative in mice bearing tumors]. | 1987 | 1 |
About Gang Yu
Gang Yu is a scholar working on Computer Vision and Pattern Recognition, Computer Graphics and Computer-Aided Design and Media Technology, having authored 108 papers that have together received 6.2k indexed citations. Recurring topics across this work include Human Pose and Action Recognition (17 papers), Advanced Neural Network Applications (15 papers), Multimodal Machine Learning Applications (15 papers), Video Surveillance and Tracking Methods (13 papers), Advanced Image and Video Retrieval Techniques (11 papers), Domain Adaptation and Few-Shot Learning (10 papers), Video Analysis and Summarization (6 papers) and 3D Shape Modeling and Analysis (6 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (4.8k citations), Media Technology (1.0k citations) and Artificial Intelligence (1.4k citations). Gang Yu has collaborated with scholars based in China, United States and Singapore. Frequent co-authors include Changqian Yu, Jingbo Wang, Changxin Gao, Nong Sang, Chunhua Shen, Chao Peng, Junsong Yuan, Shuai Shao, Enze Xie and Yinda Xu. Their work appears in journals such as SHILAP Revista de lepidopterología, The Journal of Cell Biology and IEEE Transactions on Pattern Analysis and Machine Intelligence.
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.