DeepStyle: Multimodal Search Engine for Fashion and Interior Design

TL;DR

DeepStyle is a multimodal search engine that combines visual and textual cues to retrieve fashion and interior items, outperforming baselines and enabling more intuitive, real-life-like searches.

Contribution

We introduce DeepStyle, a novel neural network architecture that models contextual dependencies between visual and textual features for improved multimodal retrieval.

Findings

Outperforms baseline methods by 18-21% on datasets

Robust across fashion and furniture datasets

Deployed in a commercial web application

Abstract

In this paper, we propose a multimodal search engine that combines visual and textual cues to retrieve items from a multimedia database aesthetically similar to the query. The goal of our engine is to enable intuitive retrieval of fashion merchandise such as clothes or furniture. Existing search engines treat textual input only as an additional source of information about the query image and do not correspond to the real-life scenario where the user looks for 'the same shirt but of denim'. Our novel method, dubbed DeepStyle, mitigates those shortcomings by using a joint neural network architecture to model contextual dependencies between features of different modalities. We prove the robustness of this approach on two different challenging datasets of fashion items and furniture where our DeepStyle engine outperforms baseline methods by 18-21% on the tested datasets. Our search engine is commercially deployed and available through a Web-based application.