Two-stage attribute-guided dual attention network for fine-grained fashion retrieval

Abstract

Fine-grained clothing retrieval is essential for intelligent shopping and personalized recommendation systems. However, conventional methods often fail to capture subtle attribute variations. This paper proposes a novel two-stage attribute-guided dual attention network. The network combines global and local feature extraction with Attribute-aware Multi-Scale Spatial Attention (AMSA) and Attribute-guided Dynamic Channel Attention (ADCA). AMSA captures attribute-specific spatial details at multiple scales, while ADCA dynamically adjusts channel importance based on attribute embeddings, enabling precise attribute-level similarity modeling. A multi-level joint loss function further optimizes both global and local representations and enhances feature alignment. Experiments on FashionAI and the self-built FGDress dataset show that the proposed method achieves mAP scores of 66.01% and 73.98%, respectively, outperforming baseline approaches. Attribute-level analysis confirms robust recognition of both well-defined and challenging attributes. These results validate the practicality and generalizability of the proposed framework, with promising applications in personalized recommendation, fashion trend analysis, and design evaluation.