Yanqing Shen

Papers

Total Citations

177

H-Index

About

Yanqing Shen is a researcher specializing in computer vision and autonomous systems, with a particular focus on visual place recognition (VPR) — a critical capability enabling machines to identify and navigate familiar locations in complex, real-world environments. Shen's most prominent contribution is the development of **TransVPR**, a transformer-based framework that leverages multi-level attention aggregation to tackle one of the field's most persistent challenges: the interference caused by distracting visual elements in dynamic scenes. By harnessing the power of transformer architectures, TransVPR offers a more robust and context-aware approach to scene understanding, directly benefiting applications such as autonomous vehicle navigation and mobile robot localization. The work has garnered significant attention from the research community, accumulating over 170 citations since its publication in 2022 — a strong indicator of its relevance and influence within the field. Shen's research sits at the intersection of deep learning and robotic perception, addressing real-world deployment challenges where visual reliability is paramount. For students and researchers working on autonomous systems, localization, or attention-based vision models, Shen's contributions represent a meaningful and timely advancement in how machines understand and remember the visual world around them.