mlsanityData to 14 Oct 2026

AI Tailoring: Evaluating Influence of Image Features on Fashion Product Popularity

arXiv 2411.14737 ↗First posted 22 Nov 2024cs.CV cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 13 of its 73 references that are in the map. First posted on arXiv 22 Nov 2024.

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References · 13

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  11. 2024Selection of LLM Fine-Tuning Data based on Orthogonal Rulescited by 12
  12. 2016Fashion DNA: Merging Content and Sales Data for Recommendation and Article Mappingcited by 10
1 more
  1. 2017Style2Vec: Representation Learning for Fashion Items from Style Setscited by 8

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  1. 2024Dif4FF: Leveraging Multimodal Diffusion Models and Graph Neural Networks for Accurate New Fashion Product Performance Forecasting
  2. 2024Automatic Generation of Fashion Images using Prompting in Generative Machine Learning Models
  3. 2023SGDiff: A Style Guided Diffusion Model for Fashion Synthesis
  4. 2024FashionSD-X: Multimodal Fashion Garment Synthesis using Latent Diffusion
  5. 2024Diffusion Models for Generative Outfit Recommendation
  6. 2024MDiFF: Exploiting Multimodal Score-based Diffusion Models for New Fashion Product Performance Forecasting
  7. 2026AesRec: A Dataset for Aesthetics-Aligned Clothing Outfit Recommendation ↗
  8. 2025Fashion-RAG: Multimodal Fashion Image Editing via Retrieval-Augmented Generation