An End-to-End Attention-Based Neural Model for Complementary Clothing Matching

In modern society, people tend to prefer fashionable and decent outfits that can meet more than basic physiological needs. In fact, a proper outfit usually relies on good matching among complementary fashion items (e.g., the top, bottom, and shoes) that compose it, which thus propels us to investigate the automatic complementary clothing matching scheme. However, this is non-trivial due to the following challenges. First, the main challenge lies in how to accurately model the compatibility between complementary fashion items (e.g., the top and bottom) that come from the heterogeneous spaces with multi-modalities (e.g., the visual modality and textual modality). Second, since different features (e.g., the color, style, and pattern) of fashion items may contribute differently to compatibility modeling, how to encode the confidence of different pairwise features presents a tough challenge. Third, how to jointly learn the latent representation of multi-modal data and the compatibility between complementary fashion items contributes to the last challenge. Toward this end, in this work, we present an end-to-end attention-based neural framework for the compatibility modeling, where we introduce a feature-level attention model to adaptively learn the confidence for different pairwise features. Extensive experiments on a public available real-world dataset show the superiority of our model over state-of-the-art methods.