4 papers
Don't Miss the Forest for the Trees: Attentional Vision Calibration for Large Vision Language Models
Sangmin Woo, Donguk Kim, Jaehyuk Jang +2
Large Vision Language Models (LVLMs) demonstrate strong capabilities in visual understanding and description, yet often suffer from hallucinations, attributing incorrect or mislead…
Parameter Efficient Mamba Tuning via Projector-targeted Diagonal-centric Linear Transformation
Seokil Ham, Hee-Seon Kim, Sangmin Woo +1
Despite the growing interest in Mamba architecture as a potential replacement for Transformer architecture, parameter-efficient fine-tuning (PEFT) approaches for Mamba remain large…
RITUAL: Random Image Transformations as a Universal Anti-hallucination Lever in Large Vision Language Models
Sangmin Woo, Jaehyuk Jang, Donguk Kim +2
Recent advancements in Large Vision Language Models (LVLMs) have revolutionized how machines understand and generate textual responses based on visual inputs, yet they often produc…
Diffusion Model Patching via Mixture-of-Prompts
Seokil Ham, Sangmin Woo, Jin-Young Kim +3
We present Diffusion Model Patching (DMP), a simple method to boost the performance of pre-trained diffusion models that have already reached convergence, with a negligible increas…