no code implementations • 21 Mar 2024 • Kwanyoung Kim, Yujin Oh, Jong Chul Ye
The recent success of CLIP has demonstrated promising results in zero-shot semantic segmentation by transferring muiltimodal knowledge to pixel-level classification.
no code implementations • 10 Mar 2024 • Kwanyoung Kim, Jaa-Yeon Lee, Jong Chul Ye
Nakagami imaging holds promise for visualizing and quantifying tissue scattering in ultrasound waves, with potential applications in tumor diagnosis and fat fraction estimation which are challenging to discern by conventional ultrasound B-mode images.
no code implementations • 27 Nov 2023 • Kwanyoung Kim, Yujin Oh, Sangjoon Park, Hwa Kyung Byun, Jin Sung Kim, Yong Bae Kim, Jong Chul Ye
Recent advancements in Artificial Intelligence (AI) have profoundly influenced medical fields, by providing tools to reduce clinical workloads.
1 code implementation • 24 May 2023 • Beomsu Kim, Gihyun Kwon, Kwanyoung Kim, Jong Chul Ye
Diffusion models are a powerful class of generative models which simulate stochastic differential equations (SDEs) to generate data from noise.
no code implementations • 28 Jan 2023 • Kwanyoung Kim, Yujin Oh, Jong Chul Ye
In particular, we introduce a novel Multiple Prompt Optimal Transport Solver (MPOT), which is designed to learn an optimal mapping between multiple text prompts and visual feature maps of the frozen image encoder hidden layers.
no code implementations • CVPR 2022 • Kwanyoung Kim, Taesung Kwon, Jong Chul Ye
Through extensive experiments, we demonstrate that the proposed method can accurately estimate noise models and parameters, and provide the state-of-the-art self-supervised image denoising performance in the benchmark dataset and real-world dataset.
no code implementations • NeurIPS 2021 • Kwanyoung Kim, Jong Chul Ye
Recently, there has been extensive research interest in training deep networks to denoise images without clean reference. However, the representative approaches such as Noise2Noise, Noise2Void, Stein's unbiased risk estimator (SURE), etc.
1 code implementation • 13 Jun 2021 • Kwanyoung Kim, Jong Chul Ye
Recently, there has been extensive research interest in training deep networks to denoise images without clean reference.
no code implementations • CVPR 2021 • Kwanyoung Kim, Dongwon Park, Kwang In Kim, Se Young Chun
Often, labeling large amount of data is challenging due to high labeling cost limiting the application domain of deep learning techniques.
no code implementations • 18 Dec 2018 • Kwanyoung Kim, Se Young Chun
Recently, SRGAN was proposed to avoid this average effect by minimizing perceptual losses instead of l1 loss and it yielded perceptually better SR images (or images with sharp edges) at the price of lowering PSNR.