Papers
8
Total Citations
567
H-Index
6
About
Ling Shao is a leading figure in computer vision and intelligent systems, whose research spans 3D human pose estimation, RGB-D sensing, and remote sensing scene classification. His highly cited 2021 review on deep 3D human pose estimation (383 citations) has become a foundational resource, systematically addressing the challenges of estimating articulated joint locations from images and video—a critical capability for human motion analysis, human-computer interaction, and robotics. Shao also pioneered work on RGB-D sensors, notably editing a special issue on Kinect applications (55 citations) that advanced depth-sensing technology for widespread use. His bioinspired scene classification framework, integrating deep active learning for remote sensing (49 citations), demonstrates his commitment to bridging biological inspiration with practical AI. Additionally, Shao has contributed to building recognition in urban environments, action recognition via body pose correlograms, and cross-modal vision-language mapping. His work consistently emphasizes real-world deployment in robotics and autonomous systems, making him a key contributor to the evolution of intelligent visual perception.
Research Focus
Key Achievements
Top Papers
- 1Deep 3D human pose estimation: A review383 citations · 2021
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- 5Action recognition using Correlogram of Body Poses and spectral regression26 citations · 2011
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- 8Semi-supervised vision-language mapping via variational learning5 citations · 2017