Discovering Underground Maps from Fashion

Utkarsh Mall1, 2Kavita Bala1Tamara Berg3Kristen Grauman2, 4

1Cornell University2Facebook AI3Facebook4UT 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.

@inproceedings{underground-22,
 title={Discovering Underground Maps from Fashion},
 author={Mall, Utkarsh and Bala, Kavita and Berg, Tamara and Grauman, Kristen},
 booktitle={WACV},
 year={2022}
}

Poster

Coming Soon!

Code

Coming Soon!

Data

Coming Soon!

Video

Video showing t-SNE visualization of progression of learning using t-SNE.

Acknowledgements

This work was part of an internship at Facebook AI Research. We also thank TCS for supporting us.