  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 everyday images of people wearing clothes.
We introduce an approach that detects which cities influence which other cities
in terms of propagating their styles. We then leverage the discovered influence
patterns to inform a forecasting model that predicts the popularity of any
given style at any given city into the future. Demonstrating our idea with
GeoStyle---a large-scale dataset of 7.7M images covering 44 major world cities,
we present the discovered influence relationships, revealing how cities exert
and receive fashion influence for an array of 50 observed visual styles.
Furthermore, the proposed forecasting model achieves state-of-the-art results
for a challenging style forecasting task, showing the advantage of grounding
visual style evolution both spatially and temporally.
