AI定制：评估图像特征对时尚产品流行度的影响
AI Tailoring: Evaluating Influence of Image Features on Fashion Product Popularity
AI总结：
本文提出影响力分数指标和结合Transformer与随机森林的时尚需求预测器，利用历史销售数据识别影响时尚产品流行度的关键图像特征，并通过图像编辑消融实验和人类偏好调查验证方法有效性。
摘要：
识别影响消费者偏好的关键产品特征在时尚行业中至关重要。本研究引入一种稳健的方法，利用历史市场销售数据确定时尚产品图像中最具影响力的特征。首先，我们提出名为“影响力分数”的指标，用于定量评估产品特征的重要性。随后，我们开发了一个预测模型——时尚需求预测器（FDP），该模型结合了基于Transformer的模型和随机森林，根据产品图像预测市场流行度。我们采用图像编辑扩散模型修改这些图像并进行消融研究，验证最高分和最低分特征对模型流行度预测的影响。此外，我们通过收集人类偏好排名的调查进一步验证这些结果，确认了FDP模型预测的准确性以及我们方法在识别有影响力特征方面的有效性。值得注意的是，经过“优质”特征增强的产品在预测流行度上相比其修改后的对应产品表现出显著提升。我们的方法开发了一个全自动、系统化的时尚图像分析框架，为时尚产品设计和营销策略制定等下游任务提供了有价值的指导。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
