  Each day billions of photographs are uploaded to photo-sharing services and
social media platforms. These images are packed with information about how
people live around the world. In this paper we exploit this rich trove of data
to understand fashion and style trends worldwide. We present a framework for
visual discovery at scale, analyzing clothing and fashion across millions of
images of people around the world and spanning several years. We introduce a
large-scale dataset of photos of people annotated with clothing attributes, and
use this dataset to train attribute classifiers via deep learning. We also
present a method for discovering visually consistent style clusters that
capture useful visual correlations in this massive dataset. Using these tools,
we analyze millions of photos to derive visual insight, producing a
first-of-its-kind analysis of global and per-city fashion choices and
spatio-temporal trends.
