Discovering Underground Maps From Fashion

Utkarsh Mall, Kavita Bala, Tamara Berg, Kristen Grauman; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2022, pp. 3114-3123


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.

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@InProceedings{Mall_2022_WACV, author = {Mall, Utkarsh and Bala, Kavita and Berg, Tamara and Grauman, Kristen}, title = {Discovering Underground Maps From Fashion}, booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}, month = {January}, year = {2022}, pages = {3114-3123} }