From Lost to Found: Discover Missing UI Design Semantics through Recovering Missing Tags

TL;DR

This paper presents a neural network-based method to recover missing tags in UI design images, improving search accuracy and diversity on design sharing platforms by encoding visual and textual information.

Contribution

It introduces a novel deep learning approach that predicts missing tags for UI images, enhancing retrieval and organization in design sharing communities.

Findings

Achieved 82.72% accuracy in tag prediction.

Returned hundreds more results per query than default search.

Improved search relatedness, diversity, and user satisfaction.

Abstract

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.