FANCY: Human-centered, Deep Learning-based Framework for Fashion Style Analysis
Youngseung Jeon, Seungwan Jin, Kyungsik Han
Abstract
Fashion style analysis is of the utmost importance for fashion professionals. However, it has an issue of having different style classification criteria that rely heavily on professionals’ subjective experiences with no quantitative criteria. We present FANCY (Fashion Attributes detectioN for Clustering stYle), a human-centered, deep learning-based framework to support fashion professionals’ analytic tasks using a computational method integrated with their insights. We work closely with fashion professionals in the whole study process to reflect their domain knowledge and experience as much as possible. We redefine fashion attributes, demonstrate a strong association with fashion attributes and styles, and develop a deep learning model that detects attributes in a given fashion image and reflects fashion professionals’ insight. Based on attribute-annotated 302,772 runway fashion images, we developed 25 new fashion styles (FANCY dataset 1). We summarize quantitative standards of the fashion style groups and present fashion trends based on time, location, and brand.