Zeyi Lin
Papers
1
Total Citations
59
H-Index
1
About
Zeyi Lin is a researcher whose work lies at the intersection of computer vision and human-computer interaction, with a particular focus on hand gesture recognition using deep learning. His most-cited contribution, "STA-GCN: two-stream graph convolutional network with spatial–temporal attention for hand gesture recognition" (2020), has garnered 59 citations, establishing a foundation for more intuitive and dynamic gesture-based interfaces. In this work, Lin introduced a novel two-stream graph convolutional network architecture that integrates spatial–temporal attention mechanisms, enabling the model to selectively focus on the most informative joints and motion patterns in skeleton-based gesture data. This approach significantly improved recognition accuracy and robustness, addressing key challenges in real-world applications such as sign language interpretation and virtual reality control. By advancing the use of graph neural networks for sequential motion analysis, Lin has contributed to making human-machine communication more natural and efficient. His research continues to influence the development of intelligent systems that understand and respond to human gestures, with potential impacts spanning assistive technologies, interactive gaming, and robotic control.
Research Focus
Key Achievements
Top Papers
- 1