Zhiwen Fang

Southern Medical University, A*STAR Graduate Academy, Nanyang Technological University

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About

Dr. Zhiwen Fang is a researcher at the forefront of computer vision and robotics, specializing in human-robot interaction and few-shot learning for object detection. His work bridges the gap between visual perception and robotic understanding, enabling machines to interpret human intent through the objects they interact with. In his highly cited 2022 paper, "Multi-spectral template matching based object detection in a few-shot learning manner" (22 citations), Dr. Fang introduced an innovative approach that allows robots to recognize novel objects from just a few examples, significantly advancing adaptability in dynamic environments. His earlier foundational research, "Understanding Human-Object Interaction in RGB-D videos for Human Robot Interaction" (7 citations), established critical methods for detecting small hand-held objects—such as phones or cups—that reveal human intentions, enabling robots to respond more naturally and intelligently. Dr. Fang also contributed to practical robotic grasping with his work "Object Grasping of Humanoid Robot Based on YOLO" (9 citations). By integrating multi-spectral imaging, deep learning, and template matching, his research has opened new pathways for more intuitive and responsive robotic systems, making him a notable figure in the evolution of human-centered AI.