StreetStyle: Exploring world-wide clothing styles from millions of photos

Abstract

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.

References in corpus (2)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Automatic Construction and Natural-Language Description of Nonparametric Regression Models

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