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About

Xianghua Xu is a leading researcher at the intersection of computer vision and robotics, with key contributions in semantic segmentation, adversarial robustness, and intelligent robotic systems. Her work addresses critical challenges in autonomous driving and robotic sensing, particularly through the development of multimodal fusion techniques. In 2024, Xu introduced the Residual Spatial Fusion Network for RGB-thermal semantic segmentation, which achieved 35 citations by effectively combining visible and thermal imagery to overcome lighting limitations—a breakthrough for night-time autonomous navigation. Her 2023 study on adversarial attacks in video object segmentation (21 citations) pioneered the use of hard region discovery to expose vulnerabilities in deep neural networks, advancing security for applications like video editing and human-robot interaction. Earlier, Xu’s research on robot charging strategies (2019, 13 citations) proposed a minimum encounter time scheduling algorithm for mobile chargers, optimizing power management in warehouse automation. With a growing citation impact, Xu’s work bridges theoretical robustness and practical deployment, making her a notable figure in resilient vision systems and autonomous robotics.