生成自定义旅游杂志布局

IF 0.9 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Xiangping Wu, Shuaiwei Yao, Zheng Zhang, Jun Hu
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引用次数: 0

摘要

在指定旅游杂志的样式和元素数量的问题中,约束文本生成杂志版面的问题和约束图形版面的问题一直是一个复杂而未解决的问题。在本文中,我们通过GAN生成满足约束的文本布局。由于图形设计的复杂性和多样性,我们提高了鉴别器和生成器的性能,使生成器生成的布局更受约束。在鉴别器中加入非对应约束文本和实际布局对,提高鉴别器的性能;然后在布局编码器中加入空间注意机制,提取布局特征,生成高质量的布局。我们证明了该方法可以生成满足约束的高质量文本布局,并通过用户评分验证了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Generate custom travel magazine layouts
Among the problems of specifying the style and number of elements of a travel magazine, the problem of generating magazine layout by constraining text, and constraining graph layout remains a complex and unsolved problem. In this paper, we generate layouts of text satisfying constraints via GAN. Due to the complexity and variety of graph designs, we enhance the performance of the discriminator and the generator so that the layouts generated by the generator are more constrained. Add non-corresponding constraint text and real layout pairs to the discriminator to enhance the performance of the discriminator; then add a spatial attention mechanism to the layout encoder to extract the features of the layout and generate high-quality layouts. We demonstrate that the proposed method can generate high-quality layouts of text satisfying the constraints, and we validate the effectiveness of this method through user ratings.
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来源期刊
Intelligent Data Analysis
Intelligent Data Analysis 工程技术-计算机:人工智能
CiteScore
2.20
自引率
5.90%
发文量
85
审稿时长
3.3 months
期刊介绍: Intelligent Data Analysis provides a forum for the examination of issues related to the research and applications of Artificial Intelligence techniques in data analysis across a variety of disciplines. These techniques include (but are not limited to): all areas of data visualization, data pre-processing (fusion, editing, transformation, filtering, sampling), data engineering, database mining techniques, tools and applications, use of domain knowledge in data analysis, big data applications, evolutionary algorithms, machine learning, neural nets, fuzzy logic, statistical pattern recognition, knowledge filtering, and post-processing. In particular, papers are preferred that discuss development of new AI related data analysis architectures, methodologies, and techniques and their applications to various domains.
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