Gaofeng Meng

University of Chinese Academy of Sciences, Beijing Academy of Artificial Intelligence

Papers

Total Citations

105

H-Index

About

Gaofeng Meng is a leading researcher in computer vision and medical image analysis, with a focus on challenging segmentation tasks. His work addresses critical problems in both autonomous systems and healthcare. Meng’s most cited paper, "Enhanced Boundary Learning for Glass-like Object Segmentation" (2021, 103 citations), tackles the difficult problem of segmenting transparent objects like windows and bottles, which are notoriously hard for AI to perceive due to their variable backgrounds. This contribution has direct applications in robot navigation and grasping, advancing the field of scene understanding. More recently, Meng has ventured into medical imaging with "Force Sensing Guided Artery-Vein Segmentation via Sequential Ultrasound Images" (2024), demonstrating his versatility by integrating force sensing with ultrasound for precise vascular segmentation. This work highlights his ability to bridge computer vision with real-world sensing technologies. With over 100 citations on his most influential paper, Meng’s research is recognized for its practical impact, pushing boundaries in both autonomous robotics and clinical diagnostics. His innovative approaches continue to inspire students and researchers tackling complex visual perception problems.