DUES-Adapt:利用神经UI适应探索分布式用户体验

Ran Ju, Xingchen Zhou, Bo Xu, Weiqing Liang, Wanyi Yang, Yuan Cao, Er-yan Zhang, Ronggen Li, Yinghao Li, Ning Ding, Li Li, Ru Zhang, D. Liu
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引用次数: 1

摘要

开发人员花费大量时间来调整UI以适应不同的设备。通过从大量人类设计的UI产品中学习经验,机器可以完成适配工作。为此,我们介绍了基于AI的UI适配系统DUES-Adapt,并在本次演示中进行了展示。给定一个输入UI, DUES-Adapt解析基本UI元素,并使用解析结果为目标设备生成合理且美观的布局。UI解析和布局生成这两个AI问题是使用深度神经网络模型解决的,并使用从主流Android市场收集的超过10K个应用实例进行训练。在演示中,我们展示了许多案例,涵盖了音乐、地图、健身等许多应用,以及不同的目标终端,如平板电脑、智能手表、电视等。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
DUES-Adapt: Exploring Distributed User Experience With Neural UI Adaptation
Developers spend a great deal of time to adapt UI to different devices. By learning experience from massive number of human designed UI products, the adaptation work could be finished by machines. To this end, we introduce DUES-Adapt, an AI based UI adaptation system, and showcase in this demonstration. Given an input UI, DUES-Adapt parses the basic UI elements and employs the parsing results to generate a reasonable and aesthetic layout for a target device. The two AI problems, UI parsing and layout generation, are solved using deep neural network model and trained with over 10K app instances collected from mainstream Android markets. In the demonstration, we show a number of cases covering many apps like music, maps, fitness and different target terminals such as tablet, smartwatch, TV etc.
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