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

December 11, 2024

COMPUTER VISION

Measuring Deja Vu Memorization Efficiently

Narine Kokhlikyan, Bargav Jayaraman, Florian Bordes, Chuan Guo, Kamalika Chaudhuri

December 11, 2024

November 20, 2024

CONVERSATIONAL AI

COMPUTER VISION

Llama Guard 3 Vision: Safeguarding Human-AI Image Understanding Conversations

Jianfeng Chi, Ujjwal Karn, Hongyuan Zhan, Eric Smith, Javier Rando, Yiming Zhang, Kate Plawiak, Zacharie Delpierre Coudert, Kartikeya Upasani, Mahesh Pasupuleti

November 20, 2024

November 11, 2024

COMPUTER VISION

HOI-Swap: Swapping Objects in Videos with Hand-Object Interaction Awareness

Sherry Xue, Romy Luo, Changan Chen, Kristen Grauman

November 11, 2024

October 31, 2024

HUMAN & MACHINE INTELLIGENCE

ROBOTICS

Digitizing Touch with an Artificial Multimodal Fingertip

Mike Lambeta, Tingfan Wu, Ali Sengül, Victoria Rose Most, Nolan Black, Kevin Sawyer, Romeo Mercado, Haozhi Qi, Alexander Sohn, Byron Taylor, Norb Tydingco, Gregg Kammerer, Dave Stroud, Jake Khatha, Kurt Jenkins, Kyle Most, Neal Stein, Ricardo Chavira, Thomas Craven-Bartle, Eric Sanchez, Yitian Ding, Jitendra Malik, Roberto Calandra

October 31, 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.