no code implementations • 27 Mar 2024 • Julian Jorge Andrade Guerreiro, Naoto Inoue, Kento Masui, Mayu Otani, Hideki Nakayama
Finding a suitable layout represents a crucial task for diverse applications in graphic design.
1 code implementation • 22 Nov 2023 • Daichi Horita, Naoto Inoue, Kotaro Kikuchi, Kota Yamaguchi, Kiyoharu Aizawa
We show that a simple retrieval augmentation can significantly improve the generation quality.
1 code implementation • CVPR 2023 • Naoto Inoue, Kotaro Kikuchi, Edgar Simo-Serra, Mayu Otani, Kota Yamaguchi
Creative workflows for generating graphical documents involve complex inter-related tasks, such as aligning elements, choosing appropriate fonts, or employing aesthetically harmonious colors.
1 code implementation • CVPR 2023 • Naoto Inoue, Kotaro Kikuchi, Edgar Simo-Serra, Mayu Otani, Kota Yamaguchi
Controllable layout generation aims at synthesizing plausible arrangement of element bounding boxes with optional constraints, such as type or position of a specific element.
1 code implementation • 22 Dec 2022 • Kotaro Kikuchi, Naoto Inoue, Mayu Otani, Edgar Simo-Serra, Kota Yamaguchi
The web page colorization problem is then formalized as a task of estimating plausible color styles for a given web page content with a given hierarchical structure of the elements.
1 code implementation • 5 Jan 2021 • Naoto Inoue, Toshihiko Yamasaki
To overcome this challenge, we present SynShadow, a novel large-scale synthetic shadow/shadow-free/matte image triplets dataset and a pipeline to synthesize it.
no code implementations • 29 Feb 2020 • Takehiko Ohkawa, Naoto Inoue, Hirokatsu Kataoka, Nakamasa Inoue
Herein, we propose Augmented Cyclic Consistency Regularization (ACCR), a novel regularization method for unpaired I2I translation.
1 code implementation • 16 Dec 2019 • Ryosuke Furuta, Naoto Inoue, Toshihiko Yamasaki
However, the applications of deep reinforcement learning (RL) for image processing are still limited.
1 code implementation • 10 Nov 2018 • Ryosuke Furuta, Naoto Inoue, Toshihiko Yamasaki
This paper tackles a new problem setting: reinforcement learning with pixel-wise rewards (pixelRL) for image processing.
3 code implementations • CVPR 2018 • Naoto Inoue, Ryosuke Furuta, Toshihiko Yamasaki, Kiyoharu Aizawa
Can we detect common objects in a variety of image domains without instance-level annotations?
Ranked #5 on Weakly Supervised Object Detection on Watercolor2k (using extra training data)