  Fashion is an inherently visual concept and computer vision and artificial
intelligence (AI) are playing an increasingly important role in shaping the
future of this domain. Many research has been done on recommending fashion
products based on the learned user preferences. However, in addition to
recommending single items, AI can also help users create stylish outfits from
items they already have, or purchase additional items that go well with their
current wardrobe. Compatibility is the key factor in creating stylish outfits
from single items. Previous studies have mostly focused on modeling pair-wise
compatibility. There are a few approaches that consider an entire outfit, but
these approaches have limitations such as requiring rich semantic information,
category labels, and fixed order of items. Thus, they fail to effectively
determine compatibility when such information is not available. In this work,
we adopt a Relation Network (RN) to develop new compatibility learning models,
Fashion RN and FashionRN-VSE, that addresses the limitations of existing
approaches. FashionRN learns the compatibility of an entire outfit, with an
arbitrary number of items, in an arbitrary order. We evaluated our model using
a large dataset of 49,740 outfits that we collected from Polyvore website.
Quantitatively, our experimental results demonstrate state of the art
performance compared with alternative methods in the literature in both
compatibility prediction and fill-in-the-blank test. Qualitatively, we also
show that the item embedding learned by FashionRN indicate the compatibility
among fashion items.
