论文标题
从迷失到发现：通过恢复缺失的标签发现缺失的UI设计语义
From Lost to Found: Discover Missing UI Design Semantics through Recovering Missing Tags
论文作者
论文摘要
设计共享网站为UI设计师提供了一个平台来分享他们的作品，也有机会从他人的设计中获得灵感。为了促进管理和搜索数百万个UI设计图像，许多设计共享站点通过将分类工作分配给社区来采用协作标记系统。但是，根据我们的经验研究和对四位专业设计师的访谈，设计师通常不知道如何与紧凑的文本描述正确标记一个具有紧凑的文本描述的设计图像，从而导致不清楚，不完整且不一致的标签，以阻碍检索的示例。基于深层神经网络，我们介绍了一种新颖的方法，用于编码视觉和文本信息，以恢复现有UI示例缺少的标签，以便可以通过文本查询更容易找到它们。我们在标签预测中获得了82.72％的精度。通过对5个查询的模拟测试，我们的系统平均返回数百个结果，而默认的运输搜索的结果多数多，从而导致更好的相关性，多样性和满意度。
Design sharing sites provide UI designers with a platform to share their works and also an opportunity to get inspiration from others' designs. To facilitate management and search of millions of UI design images, many design sharing sites adopt collaborative tagging systems by distributing the work of categorization to the community. However, designers often do not know how to properly tag one design image with compact textual description, resulting in unclear, incomplete, and inconsistent tags for uploaded examples which impede retrieval, according to our empirical study and interview with four professional designers. Based on a deep neural network, we introduce a novel approach for encoding both the visual and textual information to recover the missing tags for existing UI examples so that they can be more easily found by text queries. We achieve 82.72% accuracy in the tag prediction. Through a simulation test of 5 queries, our system on average returns hundreds more results than the default Dribbble search, leading to better relatedness, diversity and satisfaction.
