77 citations · 167 across the 34 of their papers we have counts for
6 papers · 1 filter
Harnessing the Latent Diffusion Model for Training-Free Image Style Transfer
Kento Masui, Mayu Otani, Masahiro Nomura +1
Diffusion models have recently shown the ability to generate high-quality images. However, controlling its generation process still poses challenges. The image style transfer task…
Multimodal Markup Document Models for Graphic Design Completion
Kotaro Kikuchi, Ukyo Honda, Naoto Inoue +3
We introduce MarkupDM, a multimodal markup document model that represents graphic design as an interleaved multimodal document consisting of both markup language and images. Unlike…
LTSim: Layout Transportation-based Similarity Measure for Evaluating Layout Generation
Mayu Otani, Naoto Inoue, Kotaro Kikuchi +1
We introduce a layout similarity measure designed to evaluate the results of layout generation. While several similarity measures have been proposed in prior research, there has be…
Would Deep Generative Models Amplify Bias in Future Models?
Tianwei Chen, Yusuke Hirota, Mayu Otani +2
We investigate the impact of deep generative models on potential social biases in upcoming computer vision models. As the internet witnesses an increasing influx of AI-generated im…
LayoutFlow: Flow Matching for Layout Generation
Julian Jorge Andrade Guerreiro, Naoto Inoue, Kento Masui +2
Finding a suitable layout represents a crucial task for diverse applications in graphic design. Motivated by simpler and smoother sampling trajectories, we explore the use of Flow…
Towards Artwork Explanation in Large-scale Vision Language Models
Kazuki Hayashi, Yusuke Sakai, Hidetaka Kamigaito +2
Large-scale Vision-Language Models (LVLMs) output text from images and instructions, demonstrating capabilities in text generation and comprehension. However, it has not been clari…