# GeoStyle: Discovering Fashion Trends and Events

arXiv: https://arxiv.org/abs/1908.11412 · HTML: https://mlsanity.com/paper/1908.11412/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Aug 2019.

## Built on by (most cited first, 21 of 21)

- [The CLEAR Benchmark: Continual LEArning on Real-World Imagery](https://mlsanity.com/paper/2201.06289/) (2022)
- [What's in a Decade? Transforming Faces Through Time](https://mlsanity.com/paper/2210.06642/) (2022)
- [From Region to Patch: Attribute-Aware Foreground-Background Contrastive Learning for Fine-Grained Fashion Retrieval](https://mlsanity.com/paper/2305.10260/) (2023)
- [From Culture to Clothing: Discovering the World Events Behind A Century of Fashion Images](https://mlsanity.com/paper/2102.01690/) (2021)
- [Multimodal Quasi-AutoRegression: Forecasting the visual popularity of new fashion products](https://mlsanity.com/paper/2204.04014/) (2022)
- [GPS as a Control Signal for Image Generation](https://mlsanity.com/paper/2501.12390/) (2025)
- [Exploring AI in Fashion: A Review of Aesthetics, Personalization, Virtual Try-On, and Forecasting](https://mlsanity.com/paper/2101.08301/) (2021)
- [Smart Fashion: A Review of AI Applications in the Fashion & Apparel Industry](https://mlsanity.com/paper/2111.00905/) (2021)
- [Leveraging Multiple Relations for Fashion Trend Forecasting Based on Social Media](https://mlsanity.com/paper/2105.03299/) (2021)
- [The multi-modal universe of fast-fashion: the Visuelle 2.0 benchmark](https://mlsanity.com/paper/2204.06972/) (2022)
- [KE-RCNN: Unifying Knowledge based Reasoning into Part-level Attribute Parsing](https://mlsanity.com/paper/2206.10146/) (2022)
- [Shapes as Product Differentiation: Neural Network Embedding in the Analysis of Markets for Fonts](https://mlsanity.com/paper/2107.02739/) (2021)
- [VICTOR: Visual Incompatibility Detection with Transformers and Fashion-specific contrastive pre-training](https://mlsanity.com/paper/2207.13458/) (2022)
- [Social Media Fashion Knowledge Extraction as Captioning](https://mlsanity.com/paper/2309.16270/) (2023)
- [Reproducibility Companion Paper: Knowledge Enhanced Neural Fashion Trend Forecasting](https://mlsanity.com/paper/2105.11826/) (2021)
- [se-Shweshwe Inspired Fashion Generation](https://mlsanity.com/paper/2203.00435/) (2022)
- [Design-time Fashion Popularity Forecasting in VR Environments](https://mlsanity.com/paper/2212.07187/) (2022)
- [Fashionpedia-Taste: A Dataset towards Explaining Human Fashion Taste](https://mlsanity.com/paper/2305.02307/) (2023)
- [Fashionpedia-Ads: Do Your Favorite Advertisements Reveal Your Fashion Taste?](https://mlsanity.com/paper/2305.02360/) (2023)
- [UNICON: A unified framework for behavior-based consumer segmentation in e-commerce](https://mlsanity.com/paper/2309.13068/) (2023)
- [Masked Language Prompting for Generative Data Augmentation in Few-shot Fashion Style Recognition](https://mlsanity.com/paper/2504.19455/) (2025)

## Abstract (arXiv)

Understanding fashion styles and trends is of great potential interest to retailers and consumers alike. The photos people upload to social media are a historical and public data source of how people dress across the world and at different times. While we now have tools to automatically recognize the clothing and style attributes of what people are wearing in these photographs, we lack the ability to analyze spatial and temporal trends in these attributes or make predictions about the future. In this paper, we address this need by providing an automatic framework that analyzes large corpora of street imagery to (a) discover and forecast long-term trends of various fashion attributes as well as automatically discovered styles, and (b) identify spatio-temporally localized events that affect what people wear. We show that our framework makes long term trend forecasts that are >20% more accurate than the prior art, and identifies hundreds of socially meaningful events that impact fashion across the globe.