Yang Huang

National University of Defense Technology

Papers

Total Citations

H-Index

About

Yang Huang is a leading researcher in robotics and machine vision, whose work advances the integration of deep learning with autonomous systems. His primary research areas include robot target recognition, federated learning, and geometric deep learning for perception. Huang’s most influential contribution is the development of InVision, a novel robot target recognition framework that leverages deep federated learning to enhance machine vision capabilities. By introducing deep geometric learning, he significantly improved the perceptual accuracy of convolutional neural networks in dynamic environments, enabling robots to identify and track targets with greater reliability. This work, published in 2021, has already garnered 51 citations, underscoring its impact on the field. Huang’s research addresses critical challenges in distributed learning and real-time object recognition, making him a key figure in the evolution of intelligent robotic systems. His achievements are particularly notable for bridging the gap between privacy-preserving federated learning and high-performance computer vision, offering scalable solutions for next-generation autonomous platforms.