  We propose a graph neural network (GNN) approach to the problem of predicting
the stylistic compatibility of a set of furniture items from images. While most
existing results are based on siamese networks which evaluate pairwise
compatibility between items, the proposed GNN architecture exploits relational
information among groups of items. We present two GNN models, both of which
comprise a deep CNN that extracts a feature representation for each image, a
gated recurrent unit (GRU) network that models interactions between the
furniture items in a set, and an aggregation function that calculates the
compatibility score. In the first model, a generalized contrastive loss
function that promotes the generation of clustered embeddings for items
belonging to the same furniture set is introduced. Also, in the first model,
the edge function between nodes in the GRU and the aggregation function are
fixed in order to limit model complexity and allow training on smaller
datasets; in the second model, the edge function and aggregation function are
learned directly from the data. We demonstrate state-of-the art accuracy for
compatibility prediction and "fill in the blank" tasks on the Bonn and
Singapore furniture datasets. We further introduce a new dataset, called the
Target Furniture Collections dataset, which contains over 6000 furniture items
that have been hand-curated by stylists to make up 1632 compatible sets. We
also demonstrate superior prediction accuracy on this dataset.
