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arXiv 2610.17841cs.IRcs.AIcs.CVcs.LG

FASHION-RQ：挖掘分层语义结构用于时尚检索的自动评估

FASHION-RQ: Mining Hierarchical Semantic Structure for Automated Evaluation of Fashion Retrieval

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Tripti Shukla, Shwetha Somasundaram, Akshat Gupta, Rohit Garg, Paul Duff, Ian Anderson

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AI总结：

针对时尚检索系统评估缺乏合适基准的问题，提出FASHION-RQ框架，利用RQ-VAE构建可扩展基准FASHION-RQBENCH，通过多任务与指标评估模型，验证其有效性并将公开代码与数据集。

摘要：

评估时尚检索系统仍具挑战性，因为缺乏能捕捉真实搜索与推荐中细粒度、开放式查询的基准。现有基准常依赖人工整理、预定义分类法或合成提示，限制了可扩展性和语义覆盖范围。我们提出FASHION-RQ：挖掘分层语义结构用于时尚检索的自动评估，这一自动框架可直接从商品目录结构中推导用户类型查询和相关性集合。我们的核心见解是，由残差量化变分自编码器（Residual Quantized Variational Autoencoders，RQ-VAE）学习到的分层离散表示，能将商品按逐步细化的粒度组织成语义连贯的聚类。我们利用该结构构建了FASHION-RQBENCH，这是一个无需人工查询标注或预定义分类法的可扩展基准。我们提出四项互补任务，用于评估排序、相关性分类、细粒度同类别区分和 paraphrase 鲁棒性，同时辅以查询级和属性级相关性指标。对近期检索模型的实验表明，FASHION-RQBENCH可区分模型在不同查询类型和属性上的能力。一项人类研究进一步显示，该基准查询的自然度与真实用户查询相当，且意图清晰度更高，支持其用于真实世界评估的有效性。代码和FASHION-RQBENCH数据集将在论文接收后公开。

英文摘要：

Evaluating fashion retrieval systems remains challenging due to the limited availability of benchmarks that capture the fine-grained, open-ended queries encountered in real-world search and recommendation. Existing benchmarks often rely on manual curation, predefined taxonomies, or synthetic prompts, limiting scalability and semantic coverage. We introduce FASHION-RQ: Mining Hierarchical Semantic Structure for Automated Evaluation of Fashion Retrieval, an automated framework that derives user-type queries and relevance sets directly from catalog structure. Our key insight is that hierarchical discrete representations learned by Residual Quantized Variational Autoencoders (RQ-VAE) organize products into semantically coherent clusters at progressively finer granularities. We leverage this structure to construct FASHION-RQBENCH, a scalable benchmark without manual query annotation or predefined taxonomies. We propose four complementary tasks for evaluating ranking, relevance classification, fine-grained sibling discrimination, and paraphrase robustness, alongside query- and attribute-level relevance metrics. Experiments across recent retrieval models demonstrate that FASHION-RQBENCH differentiates model capabilities across query types and attributes. A human study further shows that benchmark queries achieve comparable naturalness and higher intent clarity than real user queries, supporting their validity for real-world evaluation. The code and FASHION-RQBENCH dataset will be publicly released upon acceptance.

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。

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