17 citations · 22 across the 13 of their papers we have counts for
15 papers
Recursive Think-Answer Process for LLMs and VLMs
Byung-Kwan Lee, Youngchae Chee, Yong Man Ro
Think-Answer reasoners such as DeepSeek-R1 have made notable progress by leveraging interpretable internal reasoning. However, despite the frequent presence of self-reflective cues…
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…
GenRecal: Generation after Recalibration from Large to Small Vision-Language Models
Byung-Kwan Lee, Ryo Hachiuma, Yong Man Ro +2
Recent advancements in vision-language models (VLMs) have leveraged large language models (LLMs) to achieve performance on par with closed-source systems like GPT-4V. However, depl…
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…
TroL: Traversal of Layers for Large Language and Vision Models
Byung-Kwan Lee, Sangyun Chung, Chae Won Kim +2
Large language and vision models (LLVMs) have been driven by the generalization power of large language models (LLMs) and the advent of visual instruction tuning. Along with scalin…
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…
CoLLaVO: Crayon Large Language and Vision mOdel
Byung-Kwan Lee, Beomchan Park, Chae Won Kim +1
The remarkable success of Large Language Models (LLMs) and instruction tuning drives the evolution of Vision Language Models (VLMs) towards a versatile general-purpose model. Yet,…
Causal Unsupervised Semantic Segmentation
Junho Kim, Byung-Kwan Lee, Yong Man Ro
Unsupervised semantic segmentation aims to achieve high-quality semantic grouping without human-labeled annotations. With the advent of self-supervised pre-training, various framew…
Mitigating Adversarial Vulnerability through Causal Parameter Estimation by Adversarial Double Machine Learning
Byung-Kwan Lee, Junho Kim, Yong Man Ro
Adversarial examples derived from deliberately crafted perturbations on visual inputs can easily harm decision process of deep neural networks. To prevent potential threats, variou…
Demystifying Causal Features on Adversarial Examples and Causal Inoculation for Robust Network by Adversarial Instrumental Variable Regression
Junho Kim, Byung-Kwan Lee, Yong Man Ro
The origin of adversarial examples is still inexplicable in research fields, and it arouses arguments from various viewpoints, albeit comprehensive investigations. In this paper, w…
Masking Adversarial Damage: Finding Adversarial Saliency for Robust and Sparse Network
Byung-Kwan Lee, Junho Kim, Yong Man Ro
Adversarial examples provoke weak reliability and potential security issues in deep neural networks. Although adversarial training has been widely studied to improve adversarial ro…
Distilling Robust and Non-Robust Features in Adversarial Examples by Information Bottleneck
Junho Kim, Byung-Kwan Lee, Yong Man Ro
Adversarial examples, generated by carefully crafted perturbation, have attracted considerable attention in research fields. Recent works have argued that the existence of the robu…