  Fashion compatibility learning is important to many fashion markets such as
outfit composition and online fashion recommendation. Unlike previous work, we
argue that fashion compatibility is not only a visual appearance compatible
problem but also a theme-matters problem. An outfit, which consists of a set of
fashion items (e.g., shirt, suit, shoes, etc.), is considered to be compatible
for a "dating" event, yet maybe not for a "business" occasion. In this paper,
we aim at solving the fashion compatibility problem given specific themes. To
this end, we built the first real-world theme-aware fashion dataset comprising
14K around outfits labeled with 32 themes. In this dataset, there are more than
40K fashion items labeled with 152 fine-grained categories. We also propose an
attention model learning fashion compatibility given a specific theme. It
starts with a category-specific subspace learning, which projects compatible
outfit items in certain categories to be close in the subspace. Thanks to
strong connections between fashion themes and categories, we then build a
theme-attention model over the category-specific embedding space. This model
associates themes with the pairwise compatibility with attention, and thus
compute the outfit-wise compatibility. To the best of our knowledge, this is
the first attempt to estimate outfit compatibility conditional on a theme. We
conduct extensive qualitative and quantitative experiments on our new dataset.
Our method outperforms the state-of-the-art approaches.
