38 citations · 111 across the 20 of their papers we have counts for
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CEmb-SAM: Segment Anything Model with Condition Embedding for Joint Learning from Heterogeneous Datasets
Dongik Shin, Beomsuk Kim, Seungjun Baek
Automated segmentation of ultrasound images can assist medical experts with diagnostic and therapeutic procedures. Although using the common modality of ultrasound, one typically n…
Energy-Based Cross Attention for Bayesian Context Update in Text-to-Image Diffusion Models
Geon Yeong Park, Jeongsol Kim, Beomsu Kim +2
Despite the remarkable performance of text-to-image diffusion models in image generation tasks, recent studies have raised the issue that generated images sometimes cannot capture…
Unpaired Image-to-Image Translation via Neural Schrödinger Bridge
Beomsu Kim, Gihyun Kwon, Kwanyoung Kim +1
Diffusion models are a powerful class of generative models which simulate stochastic differential equations (SDEs) to generate data from noise. While diffusion models have achieved…
Minimizing Trajectory Curvature of ODE-based Generative Models
Sangyun Lee, Beomsu Kim, Jong Chul Ye
Recent ODE/SDE-based generative models, such as diffusion models, rectified flows, and flow matching, define a generative process as a time reversal of a fixed forward process. Eve…