2017/05/18 by Ziad Al-Halah, Al-Halah, Ziad, Rainer Stiefelhagen +3 · 4 citations
Arts and Humanities · Computer Science · Neuroscience · #Aesthetic Perception and Analysis #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Fashion and Cultural Textiles #Generative Adversarial Networks and Image Synthesis
paper · pdf · doi:10.48550/arxiv.1705.06394
openalex publication_date 2017/05/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.