Diverse-Intent Multi-Turn Fashion Image Retrieval

• URL: http://arxiv.org/abs/2607.20291v1
• Date: Wed, 22 Jul 2026 15:38:56 GMT
• Title: Diverse-Intent Multi-Turn Fashion Image Retrieval
• Abstract summary: Real-world fashion search involves interactive retrieval across multiple turns.<n>We introduce DIM-Fashion, a benchmark of 26K multi-turn sessions constructed from 13 fashion retrieval datasets.<n>We also propose FashionAM, a framework that aligns conversational queries with a fashion-oriented gallery embedding space.
• Score: 42.88623710886849
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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