  Color compatibility is important for evaluating the compatibility of a
fashion outfit, yet it was neglected in previous studies. We bring this
important problem to researchers' attention and present a compatibility
learning framework as solution to various fashion tasks. The framework consists
of a novel way to model outfit compatibility and an innovative learning scheme.
Specifically, we model the outfits as graphs and propose a novel graph
construction to better utilize the power of graph neural networks. Then we
utilize both ground-truth labels and pseudo labels to train the compatibility
model in a weakly-supervised manner.Extensive experimental results verify the
importance of color compatibility alone with the effectiveness of our
framework. With color information alone, our model's performance is already
comparable to previous methods that use deep image features. Our full model
combining the aforementioned contributions set the new state-of-the-art in
fashion compatibility prediction.
