Discovering Underground Maps from Fashion

Utkarsh Mall^{1, 2},  Kavita Bala¹,  Tamara Berg³,  Kristen Grauman^{2, 4}

¹Cornell University,  ²Facebook AI,  ³Facebook,  ⁴UT Austin, 

In WACV 2022

Abstract

The fashion sense--meaning the clothing styles people wear--in a geographical region can reveal information about that region. For example, it can reflect the kind of activities people do there, or the type of crowds that frequently visit the region (e.g., tourist hot spot, student neighborhood, business center). We propose a method to create underground neighborhood maps of cities by analyzing how people dress. Using publicly available images from across a city, our method automatically segments the map into neighborhoods with a similar fashion sense. Our approach further allows discovering insights about a city, such as detecting distinct neighborhoods (what is the most unique region of NYC?) and answering analogy questions between cities (what is the "Downtown LA" of Bogota?). We also present two new underground map benchmarks derived from non-image data for 37 cities worldwide. Our method shows promising results on both these benchmarks as well as experiments with human judges.

Paper

[pdf]   [arXiv]   [supplementary pdf]

Utkarsh Mall, Kavita Bala, Tamara Berg and Kristen Grauman. "Discovering Underground Maps from Fashion". In WACV, 2022.

Poster

Video

5-minute presentation of our work.

Visualization

New York City

Comparison between a traditional neighborhod map and underground map for NYC. Use the slider for comparison.