  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.
