RESEARCH

COMPUTER VISION

From Paris to Berlin: Discovering Fashion Style Influences Around the World

April 01, 2020

Abstract

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.

Download the Paper

AUTHORS

Written by

Kristen Grauman

Ziad Al-Halah

Publisher

CVPR

Research Topics

Computer Vision

Related Publications

June 20, 2024

COMPUTER VISION

ICON: Incremental CONfidence for Joint Pose and Radiance Field Optimization

Weiyao Wang, Pierre Gleize, Hao Tang, Xingyu Chen, Kevin Liang, Matt Feiszli

June 20, 2024

June 17, 2024

COMPUTER VISION

Move Anything with Layered Scene Diffusion

Jiawei Ren, Frost Xu, Jerry Wu, Ziwei Liu, Tao Xiang, Antoine Toisoul

June 17, 2024

June 14, 2024

COMPUTER VISION

Decomposed evaluations of geographic disparities in text-to-image models

Abhishek Sureddy, Dishant Padalia, Nandhinee Periyakaruppa, Oindrila Saha, Adina Williams, Adriana Romero Soriano, Megan Richards, Polina Kirichenko, Melissa Hall

June 14, 2024

June 05, 2024

COMPUTER VISION

Cache Me if You Can: Accelerating Diffusion Models through Block Caching

Felix Wimbauer, Bichen Wu, Edgar Schoenfeld, Ji Hou, Zijian He, Artsiom Sanakoyeu, Peizhao Zhang, Sam Tsai, Jonas Kohler, Christian Rupprecht, Daniel Cramers, Peter Vajda, Jialiang Wang

June 05, 2024

Help Us Pioneer The Future of AI

We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.