Correlation-Aware Multimodal Fusion Network for Fashion Compatibility Modeling

The rapid growth of e-commerce and the booming online fashion industry are driving growing user demand for sophisticated, compatible fashion outfits. As an emerging multimodal information retrieval technology, fashion compatibility modeling aims to predict the compatibility degree for any given outfit and provide complementary item recommendations for incomplete outfits. Although existing research has made significant progress in exploring fashion compatibility tasks from a multimodal perspective, it has yet to fully exploit the multimodal information and correlations among fashion items. To effectively tackle these challenges, a correlation-aware multimodal fusion network for fashion compatibility modeling is proposed. Long-distance correlated visual features are investigated during multimodal processing to enhance the quality of visual features. An improved dual-interaction mechanism is used to achieve deep multimodal fusion. Furthermore, we explore both negative and multi-scale correlations to obtain complex correlations among items and thereby enhance the accuracy of fashion compatibility assessment. Extensive experiments on real-world fashion datasets demonstrate that our method outperforms existing advanced benchmark models in AUC and ACC metrics. This indicates the efficiency of our model in enhancing fashion compatibility evaluation performance.