Hyoung won Kim
Papers
1
Total Citations
3
H-Index
1
About
Hyoung won Kim is a leading researcher in computer vision and autonomous systems, with a primary focus on 3D object detection and domain adaptation. His most cited work, "Semi-Supervised Domain Adaptation Using Target-Oriented Domain Augmentation for 3D Object Detection" (2024, 3 citations), addresses a critical challenge in real-world deployment: the performance degradation of detection models caused by shifts in sensor data distribution from factors like sensor upgrades, weather changes, and geographic differences. Kim’s key contribution lies in developing a semi-supervised framework that leverages target-oriented domain augmentation to bridge the gap between source and target domains, significantly improving detection robustness without requiring extensive labeled data. This work has direct implications for autonomous driving and robotics, where reliable perception under varying conditions is essential. Despite being early in his career, Kim’s research demonstrates high impact potential, offering a scalable solution to one of the most persistent hurdles in 3D vision. His innovative approach to domain adaptation positions him as a rising figure in the field, with future work likely to further advance the reliability of autonomous systems in dynamic environments.
Research Focus
Key Achievements
Top Papers
- 1