4 accepted papers
We present a novel hierarchical modeling method for layout representation learning, the core of design documents (e.g., user interface, poster, template). Existing works on layout representation often ignore element hierarchies, which is an important facet of layouts, and mainly rely on the spatial…
We present Magic Layouts; a method for parsing screenshots or hand-drawn sketches of user interface (UI) layouts. Our core contribution is to extend existing detectors to exploit a learned structural prior for UI designs, enabling robust detection of UI components; buttons, text boxes and similar. S…
This paper proposes dynamically modulated metric learning (DMML) for learning a tiered similarity space to perform visual search. Existing methods often treat the training samples having different degree of information with equal importance which hinders in capturing the underlying granularities in…
We propose a novel representation learning technique for measuring the similarity of user interface designs. A triplet network is used to learn a search embedding for layout similarity, with a hybrid encoder-decoder backbone comprising a graph convolutional network (GCN) and convolutional decoder (C…