用图像特征预测时尚产品受欢迎程度，自动找出关键设计因素。
AI Tailoring: Evaluating Influence of Image Features on Fashion Product Popularity
- 提出'影响分值'量化图像特征重要性，结合Transformer与随机森林建模。
- 通过修改图像验证高/低分特征对销量预测的影响，效果显著提升。
- 适合时尚设计、营销策略制定者，可自动化指导产品优化。
识别影响消费者偏好的关键产品特征对时尚行业至关重要。本文提出一种稳健方法，基于历史销售数据评估时尚商品图像中的关键特征。首先引入“影响分值”量化特征重要性；随后构建融合Transformer与随机森林的时尚需求预测模型（FDP），根据图像预测市场热度。采用图像编辑扩散模型修改图像，开展消融实验，验证最高和最低分特征对预测结果的影响。此外，通过用户调查收集偏好排名，进一步验证了FDP模型预测的准确性及本方法识别关键特征的有效性。结果显示，加入“优质”特征的商品在预测受欢迎程度上显著优于原图。本研究构建了全自动、系统化的时尚图像分析框架，为产品设计与营销策略提供有力支持。
原文摘要 · Abstract (English)
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 the FDP model's predictions and the efficacy of our method in identifying influential features. Notably, products enhanced with "good" features show marked improvements in predicted popularity over their modified counterparts. Our approach develops a fully automated and systematic framework for fashion image analysis that provides valuable guidance for downstream tasks such as fashion product design and marketing strategy development.
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