Ziyi Yin

Papers

Total Citations

H-Index

About

Ziyi Yin is a researcher focused on advancing human-robot interaction (HRI) through intelligent action recognition systems. Their primary research areas include computer vision, deep learning, and real-time robotic perception. Yin’s most significant contribution is the development of an attention-oriented framework for action recognition tailored specifically to interactive scenarios—a domain that, as they note, has seen limited exploration despite broader progress in action recognition. Their work addresses the critical need for robots to accurately and swiftly interpret human gestures and movements during live collaboration. By integrating attention mechanisms, Yin’s approach enhances both the precision and computational efficiency of recognition, making it viable for real-time HRI applications. Their most-cited paper (2021) has garnered 16 citations, reflecting its growing influence in the field. This research lays essential groundwork for safer, more responsive robotic systems in settings ranging from manufacturing to assistive care. Yin’s contributions are particularly notable for bridging the gap between theoretical action recognition and practical, latency-sensitive interaction demands, marking them as an emerging voice in the future of seamless human-robot teamwork.