Search Results for author: Kwanyoung Kim

Found 10 papers, 1 papers with code

OTSeg: Multi-prompt Sinkhorn Attention for Zero-Shot Semantic Segmentation

no code implementations21 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.

Semantic Segmentation Zero-Shot Semantic Segmentation

UNICORN: Ultrasound Nakagami Imaging via Score Matching and Adaptation

no code implementations10 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.

LMM-Assisted Breast Cancer Treatment Target Segmentation with Consistency Embedding

no code implementations27 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.

Language Modelling Large Language Model +1

Unpaired Image-to-Image Translation via Neural Schrödinger Bridge

1 code implementation24 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.

Image-to-Image Translation Translation

ZegOT: Zero-shot Segmentation Through Optimal Transport of Text Prompts

no code implementations28 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.

Segmentation Semantic Segmentation +2

Noise Distribution Adaptive Self-Supervised Image Denoising using Tweedie Distribution and Score Matching

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.

Image Denoising

Noise2Score: Tweedie’s Approach to Self-Supervised Image Denoising without Clean Images

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.

Image Denoising

Noise2Score: Tweedie's Approach to Self-Supervised Image Denoising without Clean Images

no code implementations13 Jun 2021 Kwanyoung Kim, Jong Chul Ye

Recently, there has been extensive research interest in training deep networks to denoise images without clean reference.

Image Denoising

Task-Aware Variational Adversarial Active Learning

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.

Active Learning Generative Adversarial Network +1

SREdgeNet: Edge Enhanced Single Image Super Resolution using Dense Edge Detection Network and Feature Merge Network

no code implementations18 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.

Edge Detection Image Super-Resolution +1

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