StreetStyle: Exploring world-wide clothing styles from millions of photos
This work addresses the need for large-scale visual discovery of fashion trends, providing insights for researchers and industry, but it is incremental as it builds on existing deep learning and clustering methods.
The paper tackled the problem of understanding global fashion trends by analyzing millions of photos, resulting in a first-of-its-kind analysis of clothing styles and spatio-temporal patterns worldwide.
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.