no code implementations • 28 Mar 2024 • Namhyuk Ahn, Wonhyuk Ahn, KiYoon Yoo, Daesik Kim, Seung-Hun Nam
Recent progress in diffusion models has profoundly enhanced the fidelity of image generation.
no code implementations • 13 Sep 2023 • Namhyuk Ahn, Junsoo Lee, Chunggi Lee, Kunhee Kim, Daesik Kim, Seung-Hun Nam, Kibeom Hong
Recent progresses in large-scale text-to-image models have yielded remarkable accomplishments, finding various applications in art domain.
1 code implementation • ICCV 2023 • Kibeom Hong, Seogkyu Jeon, Junsoo Lee, Namhyuk Ahn, Kunhee Kim, Pilhyeon Lee, Daesik Kim, Youngjung Uh, Hyeran Byun
To deliver the artistic expression of the target style, recent studies exploit the attention mechanism owing to its ability to map the local patches of the style image to the corresponding patches of the content image.
no code implementations • 8 Jun 2023 • Jihye Back, Namhyuk Ahn, Jangho Kim
Existing pruning methods utilize the importance of each weight based on specified criteria only when searching for a sparse structure but do not utilize it during training.
1 code implementation • 24 May 2023 • Sungnyun Kim, Junsoo Lee, Kibeom Hong, Daesik Kim, Namhyuk Ahn
In this study, we aim to extend the capabilities of diffusion-based text-to-image (T2I) generation models by incorporating diverse modalities beyond textual description, such as sketch, box, color palette, and style embedding, within a single model.
no code implementations • 17 May 2023 • Kwangho Lee, Patrick Kwon, Myung Ki Lee, Namhyuk Ahn, Junsoo Lee
To enable this, we introduce a landmark-parameter morphable model (LPMM), which offers control over the facial landmark domain through a set of semantic parameters.
no code implementations • CVPR 2023 • Namhyuk Ahn, Patrick Kwon, Jihye Back, Kibeom Hong, Seungkwon Kim
In the texture decoder, we propose a texture controller, which enables a user to control stroke style and abstraction to generate diverse cartoon textures.
1 code implementation • 19 Oct 2022 • Jihye Back, Seungkwon Kim, Namhyuk Ahn
Full-body portrait stylization, which aims to translate portrait photography into a cartoon style, has drawn attention recently.
1 code implementation • 25 May 2022 • Seungkwon Kim, Chaeheon Gwak, Dohyun Kim, Kwangho Lee, Jihye Back, Namhyuk Ahn, Daesik Kim
Cartoon domain has recently gained increasing popularity.
no code implementations • 29 Sep 2021 • Jeong-Hyeon Moon, Namhyuk Ahn, Kyung-Ah Sohn
Out-of-distribution (OOD) detection has made significant progress in recent years because the distribution mismatch between the training and testing can severely deteriorate the reliability of a machine learning system. Nevertheless, the lack of precise interpretation of the in-distribution limits the application of OOD detection methods to real-world system pipielines.
Out-of-Distribution Detection Out of Distribution (OOD) Detection +1
1 code implementation • 20 Apr 2021 • JuneKyu Park, Jeong-Hyeon Moon, Namhyuk Ahn, Kyung-Ah Sohn
From a safety perspective, a machine learning method embedded in real-world applications is required to distinguish irregular situations.
1 code implementation • 30 Sep 2020 • Sijin Kim, Namhyuk Ahn, Kyung-Ah Sohn
Viewing in a different point of combining, we introduce a spatially-heterogeneous distortion dataset in which multiple corruptions are applied to the different locations of each image.
5 code implementations • 5 May 2020 • Andreas Lugmayr, Martin Danelljan, Radu Timofte, Namhyuk Ahn, Dongwoon Bai, Jie Cai, Yun Cao, Junyang Chen, Kaihua Cheng, SeYoung Chun, Wei Deng, Mostafa El-Khamy, Chiu Man Ho, Xiaozhong Ji, Amin Kheradmand, Gwantae Kim, Hanseok Ko, Kanghyu Lee, Jungwon Lee, Hao Li, Ziluan Liu, Zhi-Song Liu, Shuai Liu, Yunhua Lu, Zibo Meng, Pablo Navarrete Michelini, Christian Micheloni, Kalpesh Prajapati, Haoyu Ren, Yong Hyeok Seo, Wan-Chi Siu, Kyung-Ah Sohn, Ying Tai, Rao Muhammad Umer, Shuangquan Wang, Huibing Wang, Timothy Haoning Wu, Hao-Ning Wu, Biao Yang, Fuzhi Yang, Jaejun Yoo, Tongtong Zhao, Yuanbo Zhou, Haijie Zhuo, Ziyao Zong, Xueyi Zou
This paper reviews the NTIRE 2020 challenge on real world super-resolution.
no code implementations • 23 Apr 2020 • Namhyuk Ahn, Jaejun Yoo, Kyung-Ah Sohn
In this paper, we tackle a fully unsupervised super-resolution problem, i. e., neither paired images nor ground truth HR images.
2 code implementations • CVPR 2020 • Jaejun Yoo, Namhyuk Ahn, Kyung-Ah Sohn
The key intuition of CutBlur is to enable a model to learn not only "how" but also "where" to super-resolve an image.
1 code implementation • 6 Mar 2019 • Namhyuk Ahn, Byungkon Kang, Kyung-Ah Sohn
Recent progress in deep learning-based models has improved photo-realistic (or perceptual) single-image super-resolution significantly.
no code implementations • 28 May 2018 • Namhyuk Ahn, Byungkon Kang, Kyung-Ah Sohn
Image distortion classification and detection is an important task in many applications.
3 code implementations • ECCV 2018 • Namhyuk Ahn, Byungkon Kang, Kyung-Ah Sohn
In recent years, deep learning methods have been successfully applied to single-image super-resolution tasks.
Ranked #17 on Image Super-Resolution on BSD100 - 2x upscaling