Modeling Fashion Influence from Photos

TL;DR

This paper introduces a method to quantify and predict the spread of fashion styles across cities and brands using large-scale social media and product image data, revealing influence patterns and achieving state-of-the-art forecasting accuracy.

Contribution

It presents a novel approach to discover and model fashion influence patterns from images, enabling accurate style popularity forecasting across locations and brands.

Findings

Discovered influence relationships between cities and brands.

Achieved state-of-the-art results in style forecasting.

Quantified how styles propagate spatially and temporally.

Abstract

The evolution of clothing styles and their migration across the world is intriguing, yet difficult to describe quantitatively. We propose to discover and quantify fashion influences from catalog and social media photos. We explore fashion influence along two channels: geolocation and fashion brands. We introduce an approach that detects which of these entities influence which other entities in terms of propagating their styles. We then leverage the discovered influence patterns to inform a novel forecasting model that predicts the future popularity of any given style within any given city or brand. To demonstrate our idea, we leverage public large-scale datasets of 7.7M Instagram photos from 44 major world cities (where styles are worn with variable frequency) as well as 41K Amazon product photos (where styles are purchased with variable frequency). Our model learns directly from the image data how styles move between locations and how certain brands affect each other's designs in a predictable way. The discovered influence relationships reveal how both cities and brands exert and receive fashion influence for an array of visual styles inferred from the images. Furthermore, the proposed forecasting model achieves state-of-the-art results for challenging style forecasting tasks. Our results indicate the advantage of grounding visual style evolution both spatially and temporally, and for the first time, they quantify the propagation of inter-brand and inter-city influences.