Kevin Lin
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- Advanced Image and Video Retrieval Techniques 7
- Video Surveillance and Tracking Methods 3
- Image Retrieval and Classification Techniques 3
- Human Pose and Action Recognition 3
- Advanced Neural Network Applications 3
- Media Technology top 10%
- Artificial Intelligence top 10%
- Domain Adaptation and Few-Shot Learning 4
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- Robotics and Sensor-Based Localization 4
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- Indoor and Outdoor Localization Technologies 1
- Co-authors
- Chu‐Song ChenHuei‐Fang YangJiwen LuJie ZhouMing–Ting SunJen-Hao HsiaoKuan-Hsien LiuKun Luo
- Journals
- IEEE Transactions on Pattern Analysis and Machine Intelligence (2 papers)IEEE Transactions on Neural Networks and Learning Systems (1 paper)IEEE Transactions on Circuits and Systems for Video Technology (1 paper)
- Partner nations
- TaiwanUnited StatesUnited Kingdom
In The Last Decade
Kevin Lin
12 papers receiving 642 citations
Hit Papers
Peers
Comparison fields: 5 of 69
- Computer Vision and Pattern Recognition 599
- Media Technology 43
- Artificial Intelligence 121
- Aerospace Engineering 59
- Human-Computer Interaction 10
Countries citing papers authored by Kevin Lin
This map shows the geographic impact of Kevin Lin'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 Kevin Lin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kevin Lin more than expected).
Fields of papers citing papers by Kevin Lin
This network shows the impact of papers produced by Kevin Lin. 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 Kevin Lin. The network helps show where Kevin Lin may publish in the future.
Co-authorship network
The 21 scholars most cited alongside Kevin Lin, 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 | 2023 | 2 | |
| 3 | 2021 | 3 | |
| 4 | 2020 | 50 | |
| 5 | Cross-Domain Complementary Learning with Synthetic Data for Multi-Person Part Segmentation. | 2019 | 3 |
| 6 | 2019 | 3 | |
| 7 | 2018 | 51 | |
| 8 | 2017 | 224 | |
| 9 | Learning Compact Binary Descriptors with Unsupervised Deep Neural Networksbreakdown → | 2016 | 257 |
| 10 | 2016 | 12 | |
| 11 | 2015 | 48 | |
| 12 | 2013 | 1 |
About Kevin Lin
Kevin Lin is a scholar working on Computer Vision and Pattern Recognition, Aerospace Engineering and Artificial Intelligence, having authored 12 papers that have together received 655 indexed citations. Recurring topics across this work include Advanced Image and Video Retrieval Techniques (7 papers), Domain Adaptation and Few-Shot Learning (4 papers), Robotics and Sensor-Based Localization (4 papers), Video Surveillance and Tracking Methods (3 papers), Image Retrieval and Classification Techniques (3 papers), Human Pose and Action Recognition (3 papers), Advanced Neural Network Applications (3 papers) and Indoor and Outdoor Localization Technologies (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (599 citations), Media Technology (43 citations) and Artificial Intelligence (121 citations). Kevin Lin has collaborated with scholars based in Taiwan, United States and United Kingdom. Frequent co-authors include Chu‐Song Chen, Huei‐Fang Yang, Jiwen Lu, Jie Zhou, Ming–Ting Sun, Jen-Hao Hsiao, Kuan-Hsien Liu, Kun Luo, Yinpeng Chen and Zicheng Liu. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Neural Networks and Learning Systems and IEEE Transactions on Circuits and Systems for Video Technology.
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.