Mixture of Attribute-Aware Attention Experts for Fine-grained E-Commerce Composed Image Retrieval

Composed Image Retrieval (CIR) enables users to express specific search intent by combining a reference image with manipulation text. Despite its promise for e-commerce, existing methods ignore readily available item textual descriptions, suffer from multi-attribute confounding where samples exhibit uncontrolled variations in unrelated attributes, and lack explicit fine-grained attribute modeling. We propose ${\text{MoA}}^3\text{CIR}$, featuring an asymmetric five-tower architecture that incorporates item descriptions on both query and target sides, a self-reflective data augmentation pipeline generating controlled attribute modifications through LVLM-driven refinement, and a Mixture of Attribute-Aware Attention mechanism with grouped multi-head attention and dynamic routing coupled with a two-phase training strategy. Besides, we contribute ECCIR, the first dual-modal e-commerce CIR benchmark with 110K samples featuring authentic item descriptions. Extensive experiments demonstrate that \method achieves SoTA performance on ECCIR, FashionIQ, and Shoes datasets, with substantial improvements over existing methods. Code and datasets will be made publicly available.