Papers
1
Total Citations
129
H-Index
1
About
Xiaofu Wu is a leading researcher in computer vision and autonomous driving, with a primary focus on real-time semantic segmentation for self-driving systems. His most impactful contribution is the development of AGLNet (Attention-Guided Lightweight Network), a pioneering architecture that balances accuracy and computational efficiency for onboard processing. This work, published in 2020 and cited over 129 times, introduced an innovative attention mechanism that selectively focuses on critical image regions, enabling high-performance segmentation on resource-constrained platforms. Wu’s research addresses the fundamental challenge of deploying deep learning models in real-world autonomous vehicles, where latency and power consumption are critical. By designing lightweight yet powerful networks, he has advanced the practical feasibility of self-driving perception systems. His work is widely recognized for bridging the gap between academic research and industrial deployment, making him a key figure in the autonomous driving community. Wu’s contributions continue to inspire new approaches in efficient neural network design, particularly for edge computing applications.
Research Focus
Key Achievements
Top Papers
- 1