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arXiv · 2604.09249

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

Kaidong Feng·Zhuoxuan Huang·Huizhong Guo·Yuting Jin·Xinyu Chen·Yue Liang·Yifei Gai·Li Zhou·Yunshan Ma·Zhu Sun
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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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Kaidong Feng, Zhuoxuan Huang, Huizhong Guo, Yuting Jin, Xinyu Chen, Yue Liang, Yifei Gai, Li Zhou, Yunshan Ma, Zhu Sun. 2026-04-13. FashionStylist: An Expert Knowledge-enhanced Multimodal Dataset for Fashion Understanding. https://arxiv.org/abs/2604.09249

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