Ketchup GAN: A New Dataset for Realistic Synthesis of Letters on Food

Gibran Benitez-Garcia, Keiji Yanai
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引用次数: 3

Abstract

This paper introduces a new dataset for the realistic synthesis of letters on food. Specifically, the "Ketchup GAN" dataset consists of real-world images of egg omelettes decorated with ketchup letters. Our dataset contains sufficient size and variety to train and evaluate deep learning-based generative models. In addition, we generate a synthetic ketchup-free set, which enables us to train paired-based generative adversarial networks (GAN). The ketchup GAN dataset comprises more than two thousand images of omelette dishes collected from Twitter. Automatically generated segmentation masks of egg and ketchup are also provided as part of the dataset. Thus, we can evaluate generative models based on segmentation inputs as well. With our dataset, two state-of-the-art GAN models (Pix2Pix and SPADE) are reviewed on photorealistic ketchup letter synthesis. We finally present an automatic application of omelette decoration with ketchup text input from users. The dataset and more details are publicly available at https://mm.cs.uec.ac.jp/omrice/.
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番茄酱GAN:一个用于食品上字母真实合成的新数据集
本文介绍了一种用于食品字母真实合成的新数据集。具体来说,“Ketchup GAN”数据集由用番茄酱字母装饰的蛋卷的真实图像组成。我们的数据集包含足够的大小和种类来训练和评估基于深度学习的生成模型。此外,我们生成了一个合成的无番茄酱集,这使我们能够训练基于配对的生成对抗网络(GAN)。番茄酱GAN数据集包括从推特上收集的2000多张煎蛋卷图片。自动生成的鸡蛋和番茄酱的分割掩码也作为数据集的一部分提供。因此,我们也可以评估基于分割输入的生成模型。利用我们的数据集,回顾了两种最先进的GAN模型(Pix2Pix和SPADE)在逼真的番茄酱字母合成上的应用。最后给出了一个用户输入番茄酱文字装饰煎蛋卷的自动应用。数据集和更多细节可在https://mm.cs.uec.ac.jp/omrice/上公开获取。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
1.40
自引率
16.70%
发文量
23
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