Zobrazeno 1 - 10
of 28
pro vyhledávání: '"Kim, Gwanghyun"'
Autor:
Kim, Gwanghyun, Martinez, Alonso, Su, Yu-Chuan, Jou, Brendan, Lezama, José, Gupta, Agrim, Yu, Lijun, Jiang, Lu, Jansen, Aren, Walker, Jacob, Somandepalli, Krishna
Training diffusion models for audiovisual sequences allows for a range of generation tasks by learning conditional distributions of various input-output combinations of the two modalities. Nevertheless, this strategy often requires training a separat
Externí odkaz:
http://arxiv.org/abs/2405.13762
Generating higher-resolution human-centric scenes with details and controls remains a challenge for existing text-to-image diffusion models. This challenge stems from limited training image size, text encoder capacity (limited tokens), and the inhere
Externí odkaz:
http://arxiv.org/abs/2404.04544
The increasing demand for high-quality 3D content creation has motivated the development of automated methods for creating 3D object models from a single image and/or from a text prompt. However, the reconstructed 3D objects using state-of-the-art im
Externí odkaz:
http://arxiv.org/abs/2304.02827
Recently, significant advancements have been made in 3D generative models, however training these models across diverse domains is challenging and requires an huge amount of training data and knowledge of pose distribution. Text-guided domain adaptat
Externí odkaz:
http://arxiv.org/abs/2304.01900
Autor:
Kim, Gwanghyun, Chun, Se Young
Recent 3D generative models have achieved remarkable performance in synthesizing high resolution photorealistic images with view consistency and detailed 3D shapes, but training them for diverse domains is challenging since it requires massive traini
Externí odkaz:
http://arxiv.org/abs/2211.16374
The world has suffered from COVID-19 (SARS-CoV-2) for the last two years, causing much damage and change in people's daily lives. Thus, automated detection of COVID-19 utilizing deep learning on chest computed tomography (CT) scans became promising,
Externí odkaz:
http://arxiv.org/abs/2207.01520
Autor:
Park, Sangjoon, Kim, Gwanghyun, Oh, Yujin, Seo, Joon Beom, Lee, Sang Min, Kim, Jin Hwan, Moon, Sungjun, Lim, Jae-Kwang, Park, Chang Min, Ye, Jong Chul
Although deep learning-based computer-aided diagnosis systems have recently achieved expert-level performance, developing a robust deep learning model requires large, high-quality data with manual annotation, which is expensive to obtain. This situat
Externí odkaz:
http://arxiv.org/abs/2202.06431
Federated learning, which shares the weights of the neural network across clients, is gaining attention in the healthcare sector as it enables training on a large corpus of decentralized data while maintaining data privacy. For example, this enables
Externí odkaz:
http://arxiv.org/abs/2111.01338
Recently, GAN inversion methods combined with Contrastive Language-Image Pretraining (CLIP) enables zero-shot image manipulation guided by text prompts. However, their applications to diverse real images are still difficult due to the limited GAN inv
Externí odkaz:
http://arxiv.org/abs/2110.02711
Autor:
Park, Sangjoon, Kim, Gwanghyun, Oh, Yujin, Seo, Joon Beom, Lee, Sang Min, Kim, Jin Hwan, Moon, Sungjun, Lim, Jae-Kwang, Ye, Jong Chul
Developing a robust algorithm to diagnose and quantify the severity of COVID-19 using Chest X-ray (CXR) requires a large number of well-curated COVID-19 datasets, which is difficult to collect under the global COVID-19 pandemic. On the other hand, CX
Externí odkaz:
http://arxiv.org/abs/2104.07235