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.
no code implementations • ICCV 2023 • Seogkyu Jeon, Bei Liu, Pilhyeon Lee, Kibeom Hong, Jianlong Fu, Hyeran Byun
Due to the data absence, the textual description of the target domain and the vision-language models, e. g., CLIP, are utilized to effectively guide the generator.
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.
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 • 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.
no code implementations • 20 Jul 2022 • Mirae Do, Seogkyu Jeon, Pilhyeon Lee, Kibeom Hong, Yu-seung Ma, Hyeran Byun
Domain adaptation for object detection (DAOD) has recently drawn much attention owing to its capability of detecting target objects without any annotations.
1 code implementation • 19 Aug 2021 • Seogkyu Jeon, Kibeom Hong, Pilhyeon Lee, Jewook Lee, Hyeran Byun
To these ends, we propose a novel domain generalization framework where feature statistics are utilized for stylizing original features to ones with novel domain properties.
Ranked #34 on Domain Generalization on Office-Home
1 code implementation • ICCV 2021 • Kibeom Hong, Seogkyu Jeon, Huan Yang, Jianlong Fu, Hyeran Byun
To this end, we design a novel domainness indicator that captures the domainness value from the texture and structural features of reference images.
no code implementations • 26 Feb 2021 • Seogkyu Jeon, Pilhyeon Lee, Kibeom Hong, Hyeran Byun
Face aging is the task aiming to translate the faces in input images to designated ages.
no code implementations • 11 Jan 2021 • Kibeom Hong, Youngjung Uh, Hyeran Byun
Training GANs on videos is even more sophisticated than on images because videos have a distinguished dimension: time.