打造专家标注的时尚理解数据集，支持全方位穿搭分析。
FashionStylist: An Expert Knowledge-enhanced Multimodal Dataset for Fashion Understanding
- 采用专家标注流程，提供单品与搭配的深度标注。
- 支持穿搭定位、补全和评估三类任务，覆盖复杂搭配场景。
- 适合研究多模态时尚系统与专家级推理的学者使用。
时尚理解需要视觉感知与风格、场合、搭配合理性等专家级推理能力。然而现有时尚数据集仍零散且任务导向，多聚焦于单品属性、搭配共现或弱文本监督，难以支撑对整体穿搭的深入理解。本文提出 FashionStylist，一个基于专家标注的综合性时尚理解基准数据集。通过专门设计的专家标注流程，该数据集在单品与搭配层面提供专业级标注，支持三项代表性任务：穿搭到单品的定位、穿搭补全、以及穿搭评估。这些任务涵盖复杂穿搭中多层衣物与配饰的物品还原、超越共现匹配的兼容性组合，以及对风格、季节、场合与整体协调性的专家级评价。实验表明，FashionStylist不仅可作为多种时尚任务的统一基准，还可有效提升基于多模态大模型的穿搭定位、补全与语义评估性能。
原文摘要 · Abstract (English)
Fashion understanding requires both visual perception and expert-level reasoning about style, occasion, compatibility, and outfit rationale. However, existing fashion datasets remain fragmented and task-specific, often focusing on item attributes, outfit co-occurrence, or weak textual supervision, and thus provide limited support for holistic outfit understanding. In this paper, we introduce FashionStylist, an expert-annotated benchmark for holistic and expert-level fashion understanding. Constructed through a dedicated fashion-expert annotation pipeline, FashionStylist provides professionally grounded annotations at both the item and outfit levels. It supports three representative tasks: outfit-to-item grounding, outfit completion, and outfit evaluation. These tasks cover realistic item recovery from complex outfits with layering and accessories, compatibility-aware composition beyond co-occurrence matching, and expert-level assessment of style, season, occasion, and overall coherence. Experimental results show that FashionStylist serves not only as a unified benchmark for multiple fashion tasks, but also as an effective training resource for improving grounding, completion, and outfit-level semantic evaluation in MLLM-based fashion systems.
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