28 citations · 34 across the 10 of their papers we have counts for
10 papers · 1 filter
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…
Multimodal Color Recommendation in Vector Graphic Documents
Qianru Qiu, Xueting Wang, Mayu Otani
Color selection plays a critical role in graphic document design and requires sufficient consideration of various contexts. However, recommending appropriate colors which harmonize…
Toward Verifiable and Reproducible Human Evaluation for Text-to-Image Generation
Mayu Otani, Riku Togashi, Yu Sawai +5
Human evaluation is critical for validating the performance of text-to-image generative models, as this highly cognitive process requires deep comprehension of text and images. How…
Towards Flexible Multi-modal Document Models
Naoto Inoue, Kotaro Kikuchi, Edgar Simo-Serra +2
Creative workflows for generating graphical documents involve complex inter-related tasks, such as aligning elements, choosing appropriate fonts, or employing aesthetically harmoni…
LayoutDM: Discrete Diffusion Model for Controllable Layout Generation
Naoto Inoue, Kotaro Kikuchi, Edgar Simo-Serra +2
Controllable layout generation aims at synthesizing plausible arrangement of element bounding boxes with optional constraints, such as type or position of a specific element. In th…