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A novel approach with vision-language models for custom e-commerce product listings

Multimedia Tools and Applications 2025年 1-30页

作者： Huynh Ngoc Nhu, Y. Nguyen, Quoc-Dung Kingkan, Cherdsak Applied Artificial Intelligence Institute (A2I2) Deakin University Burwood Melbourne Australia School of Engineering and Technology Asian Institute of Technology Khlong Nueng Thailand Faculty of Mechanical - Electrical and Computer Engineering School of Technology Van Lang University Ho Chi Minh City Viet Nam

This study introduces an innovative approach to enhancing e-commerce product listings through subject-driven text-to-image generation, leveraging advanced AI technologies. Focused on transforming consumer first impressions, it blends personalized visual styles with online retail needs, striking a balance between standardization and customization. The research develops a unique method for image synthesis, improving upon existing AI models such as DreamBooth and textual Inversion. This work not only equips online sellers with dynamic visual tools but also significantly enriches AI applications in e-commerce, offering both practical and academic contributions to the field. Our proposed model is evaluated based on various numerical and human-based evaluation metrics. The experimental results show that our model achieves a significant performance compared to other baseline models. Our model is further analyzed and discussed under correlation analysis, visual quality assessment, and ablation study to ensure its practical applicability and user satisfaction. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： E-commerce Generative models Human-computer interaction Personalization in retail subject-driven text-to-image synthesis Vision-language models Visual styles

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