AI Tailoring: Evaluating Influence of Image Features on Fashion Product Popularity
Xiaomin Li, Junyi Sha

TL;DR
This paper presents a systematic framework using Transformer models and diffusion image editing to identify key features influencing fashion product popularity, validated through sales data, ablation studies, and human surveys.
Contribution
It introduces the influence score metric and the Fashion Demand Predictor (FDP), a novel model combining Transformer and Random Forest for predicting fashion popularity from images.
Findings
Features with high influence scores significantly impact predicted popularity.
Image modifications based on influential features alter model predictions as expected.
Survey results confirm the model's accuracy in identifying influential fashion features.
Abstract
Identifying key product features that influence consumer preferences is essential in the fashion industry. In this study, we introduce a robust methodology to ascertain the most impactful features in fashion product images, utilizing past market sales data. First, we propose the metric called "influence score" to quantitatively assess the importance of product features. Then we develop a forecasting model, the Fashion Demand Predictor (FDP), which integrates Transformer-based models and Random Forest to predict market popularity based on product images. We employ image-editing diffusion models to modify these images and perform an ablation study, which validates the impact of the highest and lowest-scoring features on the model's popularity predictions. Additionally, we further validate these results through surveys that gather human rankings of preferences, confirming the accuracy of…
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Taxonomy
TopicsFashion and Cultural Textiles · Consumer Perception and Purchasing Behavior · Computational and Text Analysis Methods
MethodsDiffusion
