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We present a deep neural network to predict structural similarity between 2Dlayouts by leveraging Graph Matching Networks (GMN). Our network, coinedLayoutGMN, learns the layout metric via neural graph matching, using anattention-based GMN designed under a triplet network setting. To train ournetwork, we utilize weak labels obtained by pixel-wise Intersection-over-Union(IoUs) to define the triplet loss. Importantly, LayoutGMN is built with astructural bias which can effectively compensate for the lack of structureawareness in IoUs. We demonstrate this on two prominent forms of layouts, viz.,floorplans and UI designs, via retrieval experiments on large-scale datasets.In particular, retrieval results by our network better match human judgement ofstructural layout similarity compared to both IoUs and other baselinesincluding a state-of-the-art method based on graph neural networks and imageconvolution. In addition, LayoutGMN is the first deep model to offer bothmetric learning of structural layout similarity and structural matching betweenlayout elements.

LayoutGMN: Neural Graph Matching for Structural Layout Similarity