Papers
1
Total Citations
4
H-Index
1
About
Seohee Lee is a rising researcher in the field of human motion prediction and multi-modal machine learning, with a focus on advancing autonomous systems. Her work centers on pedestrian trajectory forecasting, a critical capability for autonomous driving and mobile robot navigation. Lee’s key contribution lies in developing a novel multi-modal knowledge distillation framework that integrates diverse data sources—including human pose and textual descriptions—to significantly enhance prediction accuracy. Her most-cited paper, "Multi-modal Knowledge Distillation-based Human Trajectory Forecasting" (2025), demonstrates that leveraging additional modalities extracted from camera-based perception can improve model robustness and foresight. With 4 citations already in a short time, this work signals growing recognition of her approach. Lee’s research addresses a fundamental challenge in real-world navigation: anticipating complex, uncertain human movements. By bridging computer vision and language understanding, she is helping to create safer, more intelligent autonomous agents. Her innovative distillation technique offers a practical pathway for deploying sophisticated models in resource-constrained environments, marking her as a promising contributor to the future of embodied AI and human-robot interaction.
Research Focus
Key Achievements
Top Papers
- 1Multi-modal Knowledge Distillation-based Human Trajectory Forecasting4 citations · 2025