FashionStylist: An Expert Knowledge-enhanced Multimodal Dataset for Fashion Understanding

• URL: http://arxiv.org/abs/2604.09249v2
• Date: Mon, 13 Apr 2026 09:14:45 GMT
• Title: FashionStylist: An Expert Knowledge-enhanced Multimodal Dataset for Fashion Understanding
• Abstract summary: We introduce FashionStylist, an expert-annotated benchmark for holistic and expert-level fashion understanding.<n>FashionStylist provides professionally grounded annotations at both the item and outfit levels.<n>It supports three representative tasks: outfit-to-item grounding, outfit completion, and outfit evaluation.
• Score: 14.029623403884179
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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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