Xinchen Ye
Papers
2
Total Citations
114
H-Index
2
About
Xinchen Ye is a leading researcher in computer vision and underwater imaging, whose work bridges the gap between degraded visual environments and robust machine perception. His primary research areas include depth estimation, image restoration, and 3D human pose estimation, with a strong emphasis on unsupervised learning and generative adversarial networks (GANs). Ye's most influential contribution is the development of a deep unsupervised adaptation network for joint depth estimation and color correction from monocular underwater images, published in 2019 and cited over 100 times. This work tackles the dual challenges of degraded visibility and geometrical distortion in underwater environments, enabling more accurate robotic perception and machine vision in subsea conditions—a critical advancement for marine exploration and autonomous underwater vehicles. More recently, Ye has extended his expertise to human-centric vision, introducing a real-time 3D human body pose detection and quality assessment framework assisted by GANs (2024). His research consistently demonstrates a talent for integrating deep learning with practical, real-world applications, making him a notable figure in both underwater and human pose analysis communities.
Research Focus
Key Achievements
Top Papers
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