DiSCo: Disentangled Attribute Manipulation Retrieval via Semantic Reconstruction and Consistency Regularization

The rapid evolution of the online fashion industry has intensified the demand for interactive fashion retrieval systems capable of precise and flexible searches based on user-specified attribute modifications. However, prevailing fashion retrieval methods often overlook the distinctive distributional properties of fashion images and struggle to preserve semantic consistency during attribute manipulation. To address these limitations, we propose DiSCo, a novel disentangled attribute manipulation retrieval framework via semantic reconstruction and consistency regularization. Our approach comprises three key components: (1) An attribute-aware manipulation network that constructs target fashion embeddings through cross-modal attribute modification deltas, leveraging dedicated fashion attribute encoders; (2) A cross-modal semantic reconstruction network that synthesizes target images directly from modified attribute descriptions, supervised by adversarial and attribute classification losses to ensure interpretable edits; (3) An adaptive fusion mechanism that dynamically integrates attribute-modified embeddings with reconstructed image features. Extensive evaluations on two benchmark datasets (DeepFashion and Shopping100K) demonstrate that DiSCo achieves superior retrieval accuracy over state-of-the-arts while maintaining high-fidelity editing. Quantitative and qualitative analyses further confirm that DiSCo generates more realistic fashion representations, underscoring its effectiveness in attribute-aware retrieval tasks.