Chenxing Xia
Papers
1
Total Citations
1
H-Index
1
About
Chenxing Xia is a leading researcher in computer vision and deep learning, with a primary focus on monocular depth estimation and lightweight neural network architectures. His most notable contribution is the development of LightNet, a pioneering framework for efficient monocular depth estimation that introduces high-level guidance and channel re-alignment optimization. This work, presented at ChinaMM 2025, addresses the critical challenge of balancing accuracy and computational efficiency in depth perception tasks, making it highly suitable for resource-constrained applications such as autonomous driving and mobile robotics. Although a relatively recent publication, LightNet has already garnered early citations, signaling its potential to influence future research in real-time depth sensing. Xia’s research is characterized by its emphasis on practical, deployable solutions that bridge the gap between advanced deep learning techniques and real-world hardware limitations. His work continues to inspire innovations in lightweight vision models, demonstrating a commitment to advancing both theoretical understanding and applied engineering in the field of computer vision.
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Top Papers
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