Hanming Zhai

China People's Public Security University

Papers

Total Citations

H-Index

About

Hanming Zhai is an emerging researcher in artificial intelligence and human-computer interaction, with a focus on multimodal named entity recognition (NER) and its applications in robotics. His most cited work, "MLNet: a multi-level multimodal named entity recognition architecture" (2023, 7 citations), introduces a novel framework that integrates visual and textual cues to accurately identify talking objects—a critical prerequisite for enabling robots to perform decision-making and recommendation tasks. This contribution addresses a key challenge in HCI: bridging the gap between linguistic entities and physical objects in dynamic environments. While his citation count is still growing, Zhai’s work demonstrates early impact in a niche area where precision is paramount. By advancing multimodal NER, he lays groundwork for more intuitive human-robot interactions, where machines can seamlessly interpret both speech and context. His research holds promise for applications in assistive robotics, smart environments, and interactive AI systems, marking him as a thoughtful innovator in the intersection of language understanding and embodied AI.