Feature Norm-Based Deep Network for Multi-Domain Fashion Image Retrieval

Current fashion image searching technology based on fine-grained fashion recognition on fashion images has recently achieved great success in online shopping. However, this technique is limited to a single domain-real product images-and thus is inflexible. Recognition and search performance are degraded to a large extent when the distribution of the target data is different from the source training data. To improve the flexibility of fashion image retrieval, we propose multi-domain fashion image recognition in this work. We firstly established Fashion-DA, a large-scale fashion dataset comprising 14 fashion categories and a total of 13,435 images originating from three domains. Then, we propose an unsupervised domain adaption approach based on adaptive feature norm to handle data with different feature distributions. The experiment evaluated the effectiveness of the proposed method.

Automated summary

This study introduces a multi-domain fashion image recognition approach, which improves the flexibility of fashion image retrieval by establishing the Fashion-DA dataset and using an unsupervised domain adaption method based on adaptive feature norm. The effectiveness of the proposed method is evaluated through experiments.