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About

Sanping Zhou is a computer vision researcher whose work sits at the intersection of autonomous systems, visual perception, and intelligent robotics. His research spans two primary domains: visual place recognition (VPR) and pedestrian trajectory prediction, with a particular focus on developing robust deep learning architectures that perform reliably in complex, real-world environments. Zhou's most impactful contribution is **TransVPR** (2022), a transformer-based framework for visual place recognition that employs multi-level attention aggregation to filter distracting scene elements and improve localization accuracy for autonomous vehicles and mobile robots — a work that has garnered over 172 citations, underscoring its significance to the field. He extended this line of research with **StructVPR++** (2025), which distills structural and semantic knowledge using weighted sample strategies to advance image-retrieval-based place recognition further. Beyond localization, Zhou has contributed to behavioral prediction with his **Recurrent Aligned Network** for generalized pedestrian trajectory prediction, tackling the challenging domain shift problem that hampers real-world deployment. Together, these works reflect a coherent research vision: building perception and prediction systems that generalize robustly across diverse, unpredictable environments — a cornerstone challenge for the future of autonomous intelligence.