# Fashion Forward: Forecasting Visual Style in Fashion

arXiv: https://arxiv.org/abs/1705.06394 · HTML: https://mlsanity.com/paper/1705.06394/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 May 2017.

## Built on by (most cited first, 24 of 24)

- [Conversational Fashion Image Retrieval via Multiturn Natural Language Feedback](https://mlsanity.com/paper/2106.04128/) (2021)
- [VGSE: Visually-Grounded Semantic Embeddings for Zero-Shot Learning](https://mlsanity.com/paper/2203.10444/) (2022)
- [Well Googled is Half Done: Multimodal Forecasting of New Fashion Product Sales with Image-based Google Trends](https://mlsanity.com/paper/2109.09824/) (2021)
- [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)
- [UIGR: Unified Interactive Garment Retrieval](https://mlsanity.com/paper/2204.03111/) (2022)
- [Multimodal Quasi-AutoRegression: Forecasting the visual popularity of new fashion products](https://mlsanity.com/paper/2204.04014/) (2022)
- [A Unified Meta-Learning Framework for Dynamic Transfer Learning](https://mlsanity.com/paper/2207.01784/) (2022)
- [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)
- [An Efficient Style Virtual Try on Network for Clothing Business Industry](https://mlsanity.com/paper/2105.13183/) (2021)
- [FashionSearchNet-v2: Learning Attribute Representations with Localization for Image Retrieval with Attribute Manipulation](https://mlsanity.com/paper/2111.14145/) (2021)
- [POP: Mining POtential Performance of new fashion products via webly cross-modal query expansion](https://mlsanity.com/paper/2207.11001/) (2022)
- [Image-based Virtual Fitting Room](https://mlsanity.com/paper/2104.04104/) (2021)
- [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)
- [Integrating Domain Knowledge into Large Language Models for Enhanced Fashion Recommendations](https://mlsanity.com/paper/2502.15696/) (2025)
- [Imitation Learning for Fashion Style Based on Hierarchical Multimodal Representation](https://mlsanity.com/paper/2004.06229/) (2020)
- [Partial Visual-Semantic Embedding: Fashion Intelligence System with Sensitive Part-by-Part Learning](https://mlsanity.com/paper/2211.06688/) (2022)
- [Design-time Fashion Popularity Forecasting in VR Environments](https://mlsanity.com/paper/2212.07187/) (2022)
- [Masked Language Prompting for Generative Data Augmentation in Few-shot Fashion Style Recognition](https://mlsanity.com/paper/2504.19455/) (2025)
- [NaiLIA: Multimodal Nail Design Retrieval Based on Dense Intent Descriptions and Palette Queries](https://mlsanity.com/paper/2603.05446/) (2026)

## Abstract (arXiv)

What is the future of fashion? Tackling this question from a data-driven vision perspective, we propose to forecast visual style trends before they occur. We introduce the first approach to predict the future popularity of styles discovered from fashion images in an unsupervised manner. Using these styles as a basis, we train a forecasting model to represent their trends over time. The resulting model can hypothesize new mixtures of styles that will become popular in the future, discover style dynamics (trendy vs. classic), and name the key visual attributes that will dominate tomorrow's fashion. We demonstrate our idea applied to three datasets encapsulating 80,000 fashion products sold across six years on Amazon. Results indicate that fashion forecasting benefits greatly from visual analysis, much more than textual or meta-data cues surrounding products.