Zobrazeno 1 - 10
of 474
pro vyhledávání: '"Choi, JinYoung"'
Publikováno v:
NeurIPS 2024
We propose a novel inference technique based on a pretrained diffusion model for text-conditional video generation. Our approach, called FIFO-Diffusion, is conceptually capable of generating infinitely long videos without additional training. This is
Externí odkaz:
http://arxiv.org/abs/2405.11473
Autor:
Park, Dougho, Kim, Younghun, Kang, Harim, Lee, Junmyeoung, Choi, Jinyoung, Kim, Taeyeon, Lee, Sangeok, Son, Seokil, Kim, Minsol, Kim, Injung
Publikováno v:
Computers in Biology and Medicine (2024)
Bolus segmentation is crucial for the automated detection of swallowing disorders in videofluoroscopic swallowing studies (VFSS). However, it is difficult for the model to accurately segment a bolus region in a VFSS image because VFSS images are tran
Externí odkaz:
http://arxiv.org/abs/2403.14191
We study the robustness of learned image compression models against adversarial attacks and present a training-free defense technique based on simple image transform functions. Recent learned image compression models are vulnerable to adversarial att
Externí odkaz:
http://arxiv.org/abs/2401.11902
We propose a novel diffusion-based image generation method called the observation-guided diffusion probabilistic model (OGDM), which effectively addresses the tradeoff between quality control and fast sampling. Our approach reestablishes the training
Externí odkaz:
http://arxiv.org/abs/2310.04041
Autor:
Choi, Hae-In1 (AUTHOR) chi705@naver.com, Choi, Jinyoung1 (AUTHOR) jinyoung724@naver.com, Kim, Jin Woo1 (AUTHOR) dpslzk333@naver.com, Lee, Yoon Ha1 (AUTHOR) dldbsgk926@naver.com, Cho, Kwan Hyung2 (AUTHOR) chokh@inje.ac.kr, Koo, Tae-Sung1 (AUTHOR) kootae@cnu.ac.kr
Publikováno v:
Molecules. Sep2024, Vol. 29 Issue 17, p4048. 17p.
Factors affecting the technical outcome of catheter-directed sclerotherapy for ovarian endometriomas
Autor:
Kim, Dong Kyu a, Seo, Seok Kyo b, Han, Kichang a, ⁎, Kim, Man-Deuk a, Kwon, Joon Ho a, Kim, Gyoung Min a, Kim, Hyung Cheol a, Choi, Jinyoung a, Park, Juil a, Moon, Sungmo a, Won, Jong Yun a
Publikováno v:
In European Journal of Radiology December 2024 181
We propose a versatile deep image compression network based on Spatial Feature Transform (SFT arXiv:1804.02815), which takes a source image and a corresponding quality map as inputs and produce a compressed image with variable rates. Our model covers
Externí odkaz:
http://arxiv.org/abs/2108.09551
Autor:
Choi, Jinyoung, Han, Bohyung
We propose a framework of generative adversarial networks with multiple discriminators, which collaborate to represent a real dataset more effectively. Our approach facilitates learning a generator consistent with the underlying data distribution bas
Externí odkaz:
http://arxiv.org/abs/2107.07260
Modern navigation algorithms based on deep reinforcement learning (RL) show promising efficiency and robustness. However, most deep RL algorithms operate in a risk-neutral manner, making no special attempt to shield users from relatively rare but ser
Externí odkaz:
http://arxiv.org/abs/2104.03111
Autor:
Zhang, Mingyue a, 1, Kim, Minju b, 1, Choi, Woosung c, Choi, Jinyoung c, Kim, Dong Ha b, ⁎, Liu, Yijiang d, ⁎, Lin, Zhiqun a, b, ⁎
Publikováno v:
In Progress in Polymer Science April 2024 151