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arXiv · 2505.17796

DetailFusion: A Dual-branch Framework with Detail Enhancement for Composed Image Retrieval

Yuxin Yang·Yinan Zhou·Yuxin Chen·Ziqi Zhang·Zongyang Ma·Chunfeng Yuan·Bing Li·Lin Song·Jun Gao·Peng Li·Weiming Hu
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Abstract

Composed Image Retrieval (CIR) aims to retrieve target images from a gallery based on a reference image and modification text as a combined query. Recent approaches focus on balancing global information from two modalities and encode the query into a unified feature for retrieval. However, due to insufficient attention to fine-grained details, these coarse fusion methods often struggle with handling subtle visual alterations or intricate textual instructions. In this work, we propose DetailFusion, a novel dual-branch framework that effectively coordinates information across global and detailed granularities, thereby enabling detail-enhanced CIR. Our approach leverages atomic detail variation priors derived from an image editing dataset, supplemented by a detail-oriented optimization strategy to develop a Detail-oriented Inference Branch. Furthermore, we design an Adaptive Feature Compositor that dynamically fuses global and detailed features based on fine-grained information of each unique multimodal query. Extensive experiments and ablation analyses not only demonstrate that our method achieves state-of-the-art performance on both CIRR and FashionIQ datasets but also validate the effectiveness and cross-domain adaptability of detail enhancement for CIR.

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Yuxin Yang, Yinan Zhou, Yuxin Chen, Ziqi Zhang, Zongyang Ma, Chunfeng Yuan, Bing Li, Lin Song, Jun Gao, Peng Li, Weiming Hu. 2025-05-23. DetailFusion: A Dual-branch Framework with Detail Enhancement for Composed Image Retrieval. https://arxiv.org/abs/2505.17796

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