Byung‐Kwan Lee
Papers
1
Total Citations
3
H-Index
1
About
Byung-Kwan Lee is a rising researcher in the field of vision-language models (VLMs), with a focused interest in bridging the gap between large-scale, closed-source models and efficient, open-source alternatives. His most notable contribution, the work "VLsI: Verbalized Layers-to-Interactions from Large to Small Vision Language Models," addresses a critical challenge in the field: the prohibitive computational cost of scaling VLMs. Lee proposes a novel framework that distills knowledge from large, powerful models like GPT-4V into smaller, more accessible architectures, thereby democratizing high-quality visual instruction tuning. This work, published in 2025 and already garnering 3 citations, demonstrates his early impact in a rapidly evolving area. By tackling the trade-off between model performance and computational efficiency, Lee’s research is paving the way for more practical and widely deployable vision-language systems. His focus on verbalized interactions and layer-to-layer knowledge transfer marks him as an innovator in making advanced AI capabilities more sustainable and accessible to the broader research community.
Research Focus
Key Achievements
Top Papers
- 1