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arXiv · 2607.20291

Diverse-Intent Multi-Turn Fashion Image Retrieval

Mingqiang Tang·Haokun Wen·Meng Liu·Yupeng Hu·Weili Guan·Xuemeng Song
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Abstract

Real-world fashion search involves interactive retrieval across multiple turns. However, existing multi-turn retrieval methods are built on a restrictive assumption that every interaction follows the same attribute-editing paradigm, leaving heterogeneous intent transitions unexplored. Moreover, existing approaches often rely on textification to bridge multimodal queries and visual retrieval, which may lose fine-grained visual cues. To address these gaps, we introduce DIM-Fashion, a benchmark of 26K multi-turn sessions constructed from 13 fashion retrieval datasets across 7 tasks, featuring diverse intent transitions and rollback behaviors. We further propose FashionAM, an MLLM-VLP framework that directly aligns multimodal conversational queries with a fashion-oriented gallery embedding space, avoiding intermediate textification. Extensive experiments demonstrate the effectiveness of FashionAM over existing approaches. The dataset and code will be made publicly available upon acceptance.

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Mingqiang Tang, Haokun Wen, Meng Liu, Yupeng Hu, Weili Guan, Xuemeng Song. 2026-07-22. Diverse-Intent Multi-Turn Fashion Image Retrieval. https://arxiv.org/abs/2607.20291

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