17 citations · 17 across the 5 of their papers we have counts for
9 papers · 1 filter
Unified Reinforcement and Imitation Learning for Vision-Language Models
Byung-Kwan Lee, Ryo Hachiuma, Yong Man Ro +2
Vision-Language Models (VLMs) have achieved remarkable progress, yet their large scale often renders them impractical for resource-constrained environments. This paper introduces U…
VLsI: Verbalized Layers-to-Interactions from Large to Small Vision Language Models
Byung-Kwan Lee, Ryo Hachiuma, Yu-Chiang Frank Wang +2
The recent surge in high-quality visual instruction tuning samples from closed-source vision-language models (VLMs) such as GPT-4V has accelerated the release of open-source VLMs a…
Phantom of Latent for Large Language and Vision Models
Byung-Kwan Lee, Sangyun Chung, Chae Won Kim +2
The success of visual instruction tuning has accelerated the development of large language and vision models (LLVMs). Following the scaling laws of instruction-tuned large language…
SPARK: Multi-Vision Sensor Perception and Reasoning Benchmark for Large-scale Vision-Language Models
Youngjoon Yu, Sangyun Chung, Byung-Kwan Lee +1
Large-scale Vision-Language Models (LVLMs) have significantly advanced with text-aligned vision inputs. They have made remarkable progress in computer vision tasks by aligning text…
Meteor: Mamba-based Traversal of Rationale for Large Language and Vision Models
Byung-Kwan Lee, Chae Won Kim, Beomchan Park +1
The rapid development of large language and vision models (LLVMs) has been driven by advances in visual instruction tuning. Recently, open-source LLVMs have curated high-quality vi…
MoAI: Mixture of All Intelligence for Large Language and Vision Models
Byung-Kwan Lee, Beomchan Park, Chae Won Kim +1
The rise of large language models (LLMs) and instruction tuning has led to the current trend of instruction-tuned large language and vision models (LLVMs). This trend involves eith…