  When judging style, a key question that often arises is whether or not a pair
of objects are compatible with each other. In this paper we investigate how
Siamese networks can be used efficiently for assessing the style compatibility
between images of furniture items. We show that the middle layers of pretrained
CNNs can capture essential information about furniture style, which allows for
efficient applications of such networks for this task. We also use a joint
image-text embedding method that allows for the querying of stylistically
compatible furniture items, along with additional attribute constraints based
on text. To evaluate our methods, we collect and present a large scale dataset
of images of furniture of different style categories accompanied by text
attributes.
