Fanliang Bu

China People's Public Security University

Papers

Total Citations

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About

Fanliang Bu is a rising researcher at the intersection of human-computer interaction and natural language processing, with a primary focus on multimodal named entity recognition (NER). His most cited work, "MLNet: a multi-level multimodal named entity recognition architecture" (2023, 7 citations), addresses a critical challenge in robotics: accurately identifying talking objects to enable downstream tasks like decision-making and recommendation. Bu’s key contribution lies in developing a multi-level architecture that integrates visual and textual modalities, significantly improving the precision of object determination in dynamic, real-world environments. This work is foundational for advancing context-aware AI systems, particularly in human-robot interaction scenarios. While early in his career, Bu’s research has already garnered attention for bridging the gap between linguistic entity recognition and practical robotic applications. His approach to multimodal fusion offers a scalable framework for future studies, positioning him as a promising voice in the growing field of embodied AI and interactive systems.