MontageGAN: Generation and Assembly of Multiple Components by GANs

Chean Fei Shee, Seiichi Uchida

研究成果: 書籍/レポート タイプへの寄稿会議への寄与

1 被引用数 (Scopus)

抄録

A multi-layer image is more valuable than a single-layer image from a graphic designer's perspective. However, most of the proposed image generation methods so far focus on single-layer images. In this paper, we propose MontageGAN, which is a Generative Adversarial Networks (GAN) framework for generating multi-layer images. Our method utilized a two-step approach consisting of local GANs and global GAN. Each local GAN learns to generate a specific image layer, and the global GAN learns the placement of each generated image layer. Through our experiments, we show the ability of our method to generate multi-layer images and estimate the placement of the generated image layers.

本文言語英語
ホスト出版物のタイトル2022 26th International Conference on Pattern Recognition, ICPR 2022
出版社Institute of Electrical and Electronics Engineers Inc.
ページ1478-1484
ページ数7
ISBN(電子版)9781665490627
DOI
出版ステータス出版済み - 2022
イベント26th International Conference on Pattern Recognition, ICPR 2022 - Montreal, カナダ
継続期間: 8月 21 20228月 25 2022

出版物シリーズ

名前Proceedings - International Conference on Pattern Recognition
2022-August
ISSN(印刷版)1051-4651

会議

会議26th International Conference on Pattern Recognition, ICPR 2022
国/地域カナダ
CityMontreal
Period8/21/228/25/22

!!!All Science Journal Classification (ASJC) codes

  • コンピュータ ビジョンおよびパターン認識

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