Fashion Forward: Forecasting Visual Style in Fashion

TL;DR

This paper presents an unsupervised approach to forecast future fashion styles by analyzing visual trends in images, enabling prediction of upcoming popular styles and key visual attributes.

Contribution

It introduces the first method to predict future fashion styles from images and models their trends over time in an unsupervised manner.

Findings

Visual analysis significantly outperforms textual cues in fashion forecasting

The model successfully predicts future style popularity and key visual attributes

Applied to 80,000 products over six years, demonstrating practical effectiveness

Abstract

What is the future of fashion? Tackling this question from a data-driven vision perspective, we propose to forecast visual style trends before they occur. We introduce the first approach to predict the future popularity of styles discovered from fashion images in an unsupervised manner. Using these styles as a basis, we train a forecasting model to represent their trends over time. The resulting model can hypothesize new mixtures of styles that will become popular in the future, discover style dynamics (trendy vs. classic), and name the key visual attributes that will dominate tomorrow's fashion. We demonstrate our idea applied to three datasets encapsulating 80,000 fashion products sold across six years on Amazon. Results indicate that fashion forecasting benefits greatly from visual analysis, much more than textual or meta-data cues surrounding products.