Study on Fashion Image Retrieval Methods for Efficient Fashion Visual Search

Abstract

Fashion image retrieval (FIR) is a challenging task, which requires searching for exact items accurately from massive collections of fashion products based on a query image. Despite recent advances, FIR still has limitations for application to real-world visual searches. The main reason for this is not only the trade-off between the model complexity and performance, but also the common nature of fashion images captured under uncontrolled circumstances (e.g. varying viewpoints and lighting conditions). In particular, fashion images are vulnerable to shape deformations and suffer from inconsistency between the user's query images and refined product images. Moreover, multiple fashion objects can be present simultaneously within a single image. In this paper, we considered an FIR method that is optimized for the fashion domain. We investigated training strategies and deep models to improve the retrieval performance. The experimental results on three benchmarks from DeepFashion dataset show that considered methods could achieve the significant improvements compared to the previous FIR methods.