Feiqing Zhang

Chinese Academy of Sciences, Central China Normal University

Papers

Total Citations

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About

Feiqing Zhang is a researcher at the forefront of computer vision and intelligent robotics, with a focus on self-supervised depth estimation and indoor positioning systems. In their highly cited 2021 work, "Joint Soft–Hard Attention for Self-Supervised Monocular Depth Estimation," Zhang introduced a novel attention mechanism that significantly improves the accuracy of depth maps derived from a single camera—a cost-effective alternative to expensive laser sensors. This contribution, which has garnered 10 citations, addresses a critical challenge in autonomous navigation and 3D scene understanding by enabling more reliable depth perception without labeled data. Earlier, Zhang made notable strides in educational robotics with their 2018 paper on "Positioning and guiding educational robots by using fingerprints of WiFi and RFID array." This work, cited 6 times, proposed a fusion of WiFi and RFID fingerprinting to achieve precise indoor localization, solving a key obstacle for robots delivering intelligent services in classrooms and labs. By bridging self-supervised learning with practical robotic applications, Zhang’s research demonstrates a clear impact on both algorithmic innovation and real-world deployment, making their work essential reading for students and researchers exploring affordable, scalable solutions in autonomous systems.