Younkwan Lee

Gwangju Institute of Science and Technology

Papers

Total Citations

H-Index

About

Younkwan Lee is a researcher whose work lies at the intersection of computer vision and robust visual perception, with a particular focus on developing AI systems that can see clearly in adverse weather conditions. His key research area centers on image de-raining and domain adaptation, where he has made significant contributions to enabling autonomous systems to function reliably in rain, fog, and other challenging environments. Lee’s most notable work, “Learning to See in the Rain via Disentangled Representation” (2021), introduces an innovative approach that separates rain effects from scene content, allowing neural networks to learn cleaner, more generalizable visual representations. This work has garnered attention for its potential to enhance the safety and reliability of robot vision and autonomous driving applications. With over 3 citations on this paper alone, Lee’s research is helping to bridge the gap between controlled laboratory conditions and real-world deployment, where weather remains a major obstacle. His contributions are particularly valuable for students and researchers interested in robust perception, domain generalization, and the practical challenges of deploying computer vision in the wild.