Understanding fashion styles and trends is of great potential interest to
retailers and consumers alike. The photos people upload to social media are a
historical and public data source of how people dress across the world and at
different times. While we now have tools to automatically recognize the
clothing and style attributes of what people are wearing in these photographs,
we lack the ability to analyze spatial and temporal trends in these attributes
or make predictions about the future. In this paper, we address this need by
providing an automatic framework that analyzes large corpora of street imagery
to (a) discover and forecast long-term trends of various fashion attributes as
well as automatically discovered styles, and (b) identify spatio-temporally
localized events that affect what people wear. We show that our framework makes
long term trend forecasts that are >20% more accurate than the prior art, and
identifies hundreds of socially meaningful events that impact fashion across
the globe.

本研究提供了一种自动化框架，分析大量的街头时尚图片，以发现和预测各种时尚属性的长期趋势和自动发现的风格，并确定影响人们穿着的时空本地化事件。相比之前的方法，我们的框架可以做出超过 20％的长期趋势预测，并发现了成百上千个影响全球时装的具有社会意义的事件。