Hierarchical Prompt for Task-Adaptive Composed Image Retrieval

Abstract

Composed image retrieval (CIR) seeks to retrieve target images using multi-modal queries, specifically a reference image paired with modification text. Central to CIR is integrating textual semantic modifications with visual content. Despite its importance, existing approaches typically employ a static fusion paradigm, failing to account for the semantic heterogeneity of user queries, which encompass diverse task types (e.g., addition, replacement) and var-ied content. To address these limitations, we propose the Task-Adaptive Hier-archical Prompt (TAHP) framework. TAHP guides feature extraction through dynamically generated, task-specific prompts structured at three hierarchical levels: task-type, task-content, and general prompts. Furthermore, we design a Prompt Dynamic Generation Module to adaptively synthesize prompts condi-tioned on user queries and introduce a False Negative Correction Loss to optimize cross-modal feature fusion. Extensive experiments on FashionIQ and CIRR datasets demonstrate that TAHP achieves state-of-the-art performance against existing CIR approaches.