5 papers · 1 filter
Rethinking the Use of Vision Transformers for AI-Generated Image Detection
NaHyeon Park, Kunhee Kim, Junsuk Choe +1
Rich feature representations derived from CLIP-ViT have been widely utilized in AI-generated image detection. While most existing methods primarily leverage features from the final…
Aligned but Stereotypical? How System Prompts Shape Demographic Bias in LLM-Based Text-to-Image Models
NaHyeon Park, Na Min An, Kunhee Kim +3
Text-to-image (T2I) systems increasingly rely on Large Language Model (LLM)-based text conditioning to interpret and expand user prompts. While this improves prompt understanding a…
DGQ: Distribution-Aware Group Quantization for Text-to-Image Diffusion Models
Hyogon Ryu, NaHyeon Park, Hyunjung Shim
Despite the widespread use of text-to-image diffusion models across various tasks, their computational and memory demands limit practical applications. To mitigate this issue, quan…
Improving Cone-Beam CT Image Quality with Knowledge Distillation-Enhanced Diffusion Model in Imbalanced Data Settings
Joonil Hwang, Sangjoon Park, NaHyeon Park +2
In radiation therapy (RT), the reliance on pre-treatment computed tomography (CT) images encounter challenges due to anatomical changes, necessitating adaptive planning. Daily cone…
TextBoost: Boosting Text Encoder for Personalized Text-to-Image Generation
NaHyeon Park, Kunhee Kim, Hyunjung Shim
In this paper, we introduce TextBoost, an efficient one-shot personalization approach for text-to-image diffusion models. Traditional personalization methods typically involve fine…