Fashion Forward: Forecasting Visual Style in Fashion

Ziad Al-Halah, Rainer Stiefelhagen, Kristen Grauman; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017, pp. 388-397


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.

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[pdf] [supp] [arXiv]
author = {Al-Halah, Ziad and Stiefelhagen, Rainer and Grauman, Kristen},
title = {Fashion Forward: Forecasting Visual Style in Fashion},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},
month = {Oct},
year = {2017}