# Knowledge Enhanced Neural Fashion Trend Forecasting

arXiv: https://arxiv.org/abs/2005.03297 · HTML: https://mlsanity.com/paper/2005.03297/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 May 2020.

## Built on by (most cited first, 18 of 18)

- [A Survey of Deep Learning and Foundation Models for Time Series Forecasting](https://mlsanity.com/paper/2401.13912/) (2024)
- [Well Googled is Half Done: Multimodal Forecasting of New Fashion Product Sales with Image-based Google Trends](https://mlsanity.com/paper/2109.09824/) (2021)
- [Fine-Grained Fashion Similarity Prediction by Attribute-Specific Embedding Learning](https://mlsanity.com/paper/2104.02429/) (2021)
- [From Region to Patch: Attribute-Aware Foreground-Background Contrastive Learning for Fine-Grained Fashion Retrieval](https://mlsanity.com/paper/2305.10260/) (2023)
- [FashionReGen: LLM-Empowered Fashion Report Generation](https://mlsanity.com/paper/2403.06660/) (2024)
- [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)
- [HERMES: Hybrid Error-corrector Model with inclusion of External Signals for nonstationary fashion time series](https://mlsanity.com/paper/2202.03224/) (2022)
- [Exploring AI in Fashion: A Review of Aesthetics, Personalization, Virtual Try-On, and Forecasting](https://mlsanity.com/paper/2101.08301/) (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)
- [Sentiment Analysis of Fashion Related Posts in Social Media](https://mlsanity.com/paper/2111.07815/) (2021)
- [POP: Mining POtential Performance of new fashion products via webly cross-modal query expansion](https://mlsanity.com/paper/2207.11001/) (2022)
- [Dominant Shuffle: A Simple Yet Powerful Data Augmentation for Time-series Prediction](https://mlsanity.com/paper/2405.16456/) (2024)
- [fAshIon after fashion: A Report of AI in Fashion](https://mlsanity.com/paper/2105.03050/) (2021)
- [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)
- [Masked Language Prompting for Generative Data Augmentation in Few-shot Fashion Style Recognition](https://mlsanity.com/paper/2504.19455/) (2025)

## Abstract (arXiv)

Fashion trend forecasting is a crucial task for both academia and industry. Although some efforts have been devoted to tackling this challenging task, they only studied limited fashion elements with highly seasonal or simple patterns, which could hardly reveal the real fashion trends. Towards insightful fashion trend forecasting, this work focuses on investigating fine-grained fashion element trends for specific user groups. We first contribute a large-scale fashion trend dataset (FIT) collected from Instagram with extracted time series fashion element records and user information. Further-more, to effectively model the time series data of fashion elements with rather complex patterns, we propose a Knowledge EnhancedRecurrent Network model (KERN) which takes advantage of the capability of deep recurrent neural networks in modeling time-series data. Moreover, it leverages internal and external knowledge in fashion domain that affects the time-series patterns of fashion element trends. Such incorporation of domain knowledge further enhances the deep learning model in capturing the patterns of specific fashion elements and predicting the future trends. Extensive experiments demonstrate that the proposed KERN model can effectively capture the complicated patterns of objective fashion elements, therefore making preferable fashion trend forecast.