Zhenxue Chen

Papers

Total Citations

144

H-Index

About

Zhenxue Chen is a leading researcher in computer vision and robotics, with a focus on semantic segmentation and multimodal perception. His pioneering work on fast semantic segmentation for scene perception (2018, 126 citations) addresses the critical challenge of balancing accuracy and efficiency in real-time applications like autonomous driving and robot navigation. By prioritizing computational speed without sacrificing performance, Chen’s contributions have directly influenced the development of more responsive and practical vision systems for dynamic environments. More recently, he has advanced the field of robotic perception through innovative research on visual-tactile fusion. His 2022 study on object description using visual and tactile data, along with his 2023 work on adaptive feature weighting for object classification, demonstrates how integrating multiple sensory modalities can significantly improve a robot’s ability to understand and interact with its surroundings. These contributions, though newer, are already gaining traction with 10 and 6 citations respectively. Chen’s work also extends to practical robotics applications, including force feedback and master-slave teleoperation systems for live working in hazardous environments, showcasing his commitment to solving real-world challenges.