Guotian Zeng
Papers
2
Total Citations
27
H-Index
2
About
Guotian Zeng is a researcher specializing in efficient computer vision, with a focus on real-time visual tracking for robotics and edge computing. His major contribution is the development of LiteTrack, a novel framework that combines layer pruning with asynchronous feature extraction to dramatically reduce the latency of transformer-based visual trackers. This work directly addresses the critical trade-off between high performance and real-time operation, enabling advanced tracking capabilities on resource-constrained devices. His most-cited paper, published in 2024, has already garnered 25 citations, reflecting the immediate relevance and impact of his work in the field. By tackling the challenge of deploying powerful transformer models on edge hardware, Zeng’s research paves the way for more responsive and autonomous robotic systems, from drones to mobile manipulators.
Research Focus
Key Achievements
Top Papers
- 1
- 2