Xiuzhen Duan

Anhui University of Science and Technology

Papers

Total Citations

H-Index

About

Xiuzhen Duan is a researcher in computer vision and deep learning, with a primary focus on monocular depth estimation and efficient neural network architectures. Her most notable contribution, "LightNet: A Lightweight Monocular Depth Estimation for High-Level Guidance and Channel Re-alignment Optimization," introduces a novel framework that balances accuracy and computational efficiency—a critical challenge for real-world applications like autonomous driving and augmented reality. By integrating high-level semantic guidance with channel re-alignment optimization, Duan’s work demonstrates how to achieve competitive depth estimation performance while significantly reducing model complexity and inference time. Though early in its citation trajectory, this work represents a promising step toward deployable, resource-efficient vision systems. Duan’s research addresses the growing demand for lightweight models that can operate on edge devices without sacrificing perceptual quality. Her approach to channel re-alignment and guidance mechanisms offers a fresh perspective on optimizing depth estimation pipelines. As the field pushes toward practical, real-time solutions, Duan’s contributions are positioned to influence future developments in efficient computer vision, making her a researcher to watch in the evolving landscape of lightweight deep learning.