Year: 2020
Type: article
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 catalog and social media photos. We explore fashion influence along two channels: geolocation and fashion brands. We introduce an approach that detects which of these entities influence which other entities in terms of propagating their styles. We then leverage the discovered influence patterns to inform a novel forecasting model that predicts the future popularity of any given style within any given city or brand. To demonstrate our idea, we leverage public large-scale datasets of 7.7M Instagram photos from 44 major world cities (where styles are worn with variable frequency) as well as 41K Amazon product photos (where styles are purchased with variable frequency). Our model learns directly from the image data how styles move between locations and how certain brands affect each other's designs in a predictable way. The discovered influence relationships reveal how both cities and brands exert and receive fashion influence for an array of visual styles inferred from the images. Furthermore, the proposed forecasting model achieves state-of-the-art results for challenging style forecasting tasks. Our results indicate the advantage of grounding visual style evolution both spatially and temporally, and for the first time, they quantify the propagation of inter-brand and inter-city influences. Project page:https://www.cs.utexas.edu/~ziad/influence_from_photos.html
Source: IEEE Transactions on Multimedia
Authors: Ziad Al-Halah, Kristen Grauman
Institutions: The University of Texas at Austin, Meta (United States)
Language: English
Subfield: Museology
Field: Arts and Humanities
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W3105261111