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.
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.
1 code implementation • ICCV 2023 • Byungjun Kim, Patrick Kwon, Kwangho Lee, Myunggi Lee, Sookwan Han, Daesik Kim, Hanbyul Joo
We propose a 3D generation pipeline that uses diffusion models to generate realistic human digital avatars.
no code implementations • 25 Oct 2022 • Youngin Cho, Junsoo Lee, Soyoung Yang, Juntae Kim, Yeojeong Park, Haneol Lee, Mohammad Azam Khan, Daesik Kim, Jaegul Choo
Existing deep interactive colorization models have focused on ways to utilize various types of interactions, such as point-wise color hints, scribbles, or natural-language texts, as methods to reflect a user's intent at runtime.
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 • 25 Feb 2021 • Inseop Chung, Daesik Kim, Nojun Kwak
We propose a novel method that tackles the problem of unsupervised domain adaptation for semantic segmentation by maximizing the cosine similarity between the source and the target domain at the feature level.
no code implementations • 14 Aug 2020 • Seung-Hun Nam, In-Jae Yu, Seung-Min Mun, Daesik Kim, Wonhyuk Ahn
Multi-bit watermarking (MW) has been developed to improve robustness against signal processing operations and geometric distortions.
no code implementations • 19 Nov 2019 • Daesik Kim, Gyujeong Lee, Jisoo Jeong, Nojun Kwak
In the source domain, we fully train an object detector and the RRPN with full supervision of HOI.
no code implementations • ACL 2019 • Daesik Kim, Seonhoon Kim, Nojun Kwak
Moreover, ablation studies validate that both methods of incorporating f-GCN for extracting knowledge from multi-modal contexts and our newly proposed self-supervised learning process are effective for TQA problems.
no code implementations • 9 Jul 2018 • Jeesoo Kim, Jangho Kim, Jaeyoung Yoo, Daesik Kim, Nojun Kwak
Using a subnetwork based on a precedent work of image completion, our model makes the shape of an object.
no code implementations • CVPR 2018 • Daesik Kim, Youngjoon Yoo, Jeesoo Kim, Sangkuk Lee, Nojun Kwak
In this work, we introduce a new algorithm for analyzing a diagram, which contains visual and textual information in an abstract and integrated way.
no code implementations • 2 Jul 2017 • Sangkuk Lee, Daesik Kim, Myunggi Lee, Jihye Hwang, Nojun Kwak
Through quantitative and qualitative evaluation, we show that our method is effective for retrieval of video segments using natural language queries.