Multi-Modal and Multi-Domain Embedding Learning for Fashion Retrieval and Analysis

Abstract

Big data analytics has been revolutionizing the fashion industry in recent years. This is evidenced by the fact that popular fashion brands and designers have relied on big data analytics to trace fashion trends and predict market patterns. In this paper, we propose learning a common latent feature representation from heterogeneous fashion data. Specifically, we design a multi-modal and multi-domain embedding learning framework for fashion analysis and data retrieval. Unlike most of the existing multi-view embedding methods, which only consider the heterogeneous similarity constraint, our proposed framework jointly considers both the homogeneous and heterogeneous similarity constraints to capture cross-view similarity and preserve the similarity of the same view. The proposed framework is comprised of two projection steps. In the first projection, a quintuplet-based ranking loss is proposed for multi-domain fashion data to preserve the homogeneous similarity. In the second projection, a cross-view similarity ranking loss is designed for multi-modal fashion data to capture heterogeneous similarity. By utilizing the learned common latent feature representation, the distance between any vector pairs from same or different modalities can reflect its semantic similarity. Quantitative evaluation on a new large-scale dataset and a fashion analysis case study demonstrate the effectiveness of our proposed method.