Search Results for author: Daesik Kim

Found 14 papers, 4 papers with code

Imperceptible Protection against Style Imitation from Diffusion Models

no code implementations28 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.

Image Generation

DreamStyler: Paint by Style Inversion with Text-to-Image Diffusion Models

no code implementations13 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.

Image Generation Style Transfer

AesPA-Net: Aesthetic Pattern-Aware Style Transfer Networks

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.

Semantic correspondence Style Transfer

DiffBlender: Scalable and Composable Multimodal Text-to-Image Diffusion Models

1 code implementation24 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.

Conditional Image Generation multimodal generation +1

Guiding Users to Where to Give Color Hints for Efficient Interactive Sketch Colorization via Unsupervised Region Prioritization

no code implementations25 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.

Colorization Image Colorization

Maximizing Cosine Similarity Between Spatial Features for Unsupervised Domain Adaptation in Semantic Segmentation

no code implementations25 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.

Segmentation Semantic Segmentation +1

WAN: Watermarking Attack Network

no code implementations14 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.

Textbook Question Answering with Multi-modal Context Graph Understanding and Self-supervised Open-set Comprehension

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.

Open Set Learning Question Answering +2

Vehicle Image Generation Going Well with The Surroundings

no code implementations9 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.

Colorization Image Generation +7

Dynamic Graph Generation Network: Generating Relational Knowledge from Diagrams

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.

Graph Generation Question Answering

Where to Play: Retrieval of Video Segments using Natural-Language Queries

no code implementations2 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.

Image Captioning Natural Language Queries +2

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