5 papers
Fine-Tuning Diffusion-Based Recommender Systems via Reinforcement Learning with Reward Function Optimization
Yu Hou, Hua Li, Ha Young Kim +1
Diffusion models recently emerged as a powerful paradigm for recommender systems, offering state-of-the-art performance by modeling the generative process of user-item interactions…
Learning to reason about rare diseases through retrieval-augmented agents
Ha Young Kim, Jun Li, Ana Beatriz Solana +4
Rare diseases represent the long tail of medical imaging, where AI models often fail due to the scarcity of representative training data. In clinical workflows, radiologists freque…
Leveraging the Power of MLLMs for Gloss-Free Sign Language Translation
Jungeun Kim, Hyeongwoo Jeon, Jongseong Bae +1
Sign language translation (SLT) is a challenging task that involves translating sign language images into spoken language. For SLT models to perform this task successfully, they mu…
MVFormer: Diversifying Feature Normalization and Token Mixing for Efficient Vision Transformers
Jongseong Bae, Susang Kim, Minsu Cho +1
Active research is currently underway to enhance the efficiency of vision transformers (ViTs). Most studies have focused solely on effective token mixers, overlooking the potential…
DiffSLT: Enhancing Diversity in Sign Language Translation via Diffusion Model
JiHwan Moon, Jihoon Park, Jungeun Kim +3
Sign language translation (SLT) is challenging, as it involves converting sign language videos into natural language. Previous studies have prioritized accuracy over diversity. How…