6 papers · 1 filter
Grounding Driving VLA via Inverse Kinematics
Junsung Park, Hyunjung Shim
Existing Driving VLAs predict trajectories while largely ignoring their visual tokens -- a phenomenon we trace not to insufficient training but to a structurally ill-posed task for…
SGSoft: Learning Fused Semantic-Geometric Features for 3D Shape Correspondence via Template-Guided Soft Signals
Soyeon Yoon, Chang Wook Seo, Hyunjung Shim
Learning dense correspondences across deformable 3D shapes remains a long-standing challenge due to structural variability, non-isometric deformation, and inconsistent topology. Ex…
Representation Alignment for Just Image Transformers is not Easier than You Think
Jaeyo Shin, Jiwook Kim, Hyunjung Shim
Representation Alignment (REPA) has emerged as a simple way to accelerate Diffusion Transformers training in latent space. At the same time, pixel-space diffusion transformers such…
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…
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…