5 papers
Trinity: Self-Evolving Vision-Language Models with a Self-Verifier
Youngwan Lee, Yong-Ju Lee, Sung Ju Hwang
Self-evolving vision-language models (VLMs), a form of self-improvement in which a model generates its own training data from unlabeled images, are a promising route toward agents…
Inst4DGS: Instance-Decomposed 4D Gaussian Splatting with Multi-Video Label Permutation Learning
Yonghan Lee, Dinesh Manocha
We present Inst4DGS, an instance-decomposed 4D Gaussian Splatting (4DGS) approach with long-horizon per-Gaussian trajectories. While dynamic 4DGS has advanced rapidly, instance-dec…
MultihopSpatial: Multi-hop Compositional Spatial Reasoning Benchmark for Vision-Language Model
Youngwan Lee, Soojin Jang, Yoorhim Cho +3
Spatial reasoning is foundational for Vision-Language Models (VLMs), particularly when deployed as Vision-Language-Action (VLA) agents in physical environments. However, existing b…
HoliSafe: Holistic Safety Benchmarking and Modeling for Vision-Language Model
Youngwan Lee, Kangsan Kim, Kwanyong Park +5
Despite emerging efforts to enhance the safety of Vision-Language Models (VLMs), current approaches face two main shortcomings. 1) Existing safety-tuning datasets and benchmarks on…
KOALA: Empirical Lessons Toward Memory-Efficient and Fast Diffusion Models for Text-to-Image Synthesis
Youngwan Lee, Kwanyong Park, Yoorhim Cho +2
As text-to-image (T2I) synthesis models increase in size, they demand higher inference costs due to the need for more expensive GPUs with larger memory, which makes it challenging…