Neo-Fashion: A Data-Driven Fashion Trend Forecasting System Using Catwalk Analysis

Li Zhao, Muzhen Li, Peng Sun

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Trend forecasting is a challenging and important aspect of the fashion industry. The authors design a novel fashion trend analysis system called “Neo-Fashion,” which provides recommendations to fashion researchers and practitioners about potential fashion trends using computer vision and machine learning. Neo-Fashion includes three modules, a data collection and labeling module, an instance segmentation module and a trend analysis module. Diffusion of innovation theory is used as the main theoretical framework to understand fashion trends. 32,702 catwalk images from 2019 fashion week were collected, and 769 images were labeled as training data. Neo-fashion is able to identify and segment fashion items in the given images, and indicate the fashion trends in colors, styles, clothing combinations, and other fashion attributes. To optimize the system, more data sources can be included to not only reflect trends in even more categories but also aid in understanding the trickle-up or trickle-across process in fashion.

Original languageEnglish
JournalClothing and Textiles Research Journal
DOIs
StateAccepted/In press - 2021

Bibliographical note

Publisher Copyright:
© 2021 ITAA.

Keywords

  • artificial intelligence
  • catwalk analysis
  • computer vision
  • machine learning
  • trend forecasting

ASJC Scopus subject areas

  • Materials Science (miscellaneous)
  • Business, Management and Accounting (miscellaneous)
  • Business, Management and Accounting (all)
  • Polymers and Plastics

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