通过提取与解释解读带有文本描述的兼容性关系
Deciphering Compatibility Relationships with Textual Descriptions via Extraction and Explanation
AI总结：
针对AI穿搭推荐中时尚单品兼容性解释浅显重复的问题，本研究构建了PFE成对时尚解释数据集，提出两阶段流水线微调模型，经自动与人工评估验证，其生成的解释更具知识性、贴合真实匹配关系且信息丰富。
摘要：
在人工智能驱动的穿搭推荐这一新兴领域中，理解并准确解释时尚单品之间的兼容性关系是一项具有挑战性的问题。现有模型虽在该领域取得进展，但仍偶有不足，生成的解释往往浅显且重复。本研究旨在解决这些缺陷，为此引入了Pair Fashion Explanation（PFE，成对时尚解释）数据集——这是为阐明这类兼容性关系而整理的独特资源。此外，我们提出了一种创新的两阶段流水线模型，该模型利用此数据集进行微调，使其能够生成传达单品间兼容性关系的解释。我们的实验通过自动指标和人工评估验证了该模型的潜力：它能生成具备知识性、与真实匹配相关性一致的描述，且内容易懂、信息丰富。我们的代码和数据已发布于https://github.com/wangyu-ustc/PairFashionExplanation。
英文摘要：
Understanding and accurately explaining compatibility relationships between fashion items is a challenging problem in the burgeoning domain of AI-driven outfit recommendations. Present models, while making strides in this area, still occasionally fall short, offering explanations that can be elementary and repetitive. This work aims to address these shortcomings by introducing the Pair Fashion Explanation (PFE) dataset, a unique resource that has been curated to illuminate these compatibility relationships. Furthermore, we propose an innovative two-stage pipeline model that leverages this dataset. This fine-tuning allows the model to generate explanations that convey the compatibility relationships between items. Our experiments showcase the model's potential in crafting descriptions that are knowledgeable, aligned with ground-truth matching correlations, and that produce understandable and informative descriptions, as assessed by both automatic metrics and human evaluation. Our code and data are released at https://github.com/wangyu-ustc/PairFashionExplanation
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。
