Dual Retrieval Queries Fine-Tuning for Composed Image Retrieval

Composed Image Retrieval (CIR) is a popular multi-modal retrieval task that aims to retrieve a target image based on a query composed of a reference image and modification text. The challenge lies in how to effectively retrieve a target image that preserves the visual content of the reference image while incorporating the changes described by the modification text. Existing CIR methods primarily employ a fusion-based strategy or a textual-inversion strategy during training. Although these methods have achieved promising results, they are limited in fully leveraging multi-modal information. This results in modality redundancy, where the retrieval process is dominated by one modality while ignoring the other. To address this issue, we propose an asymmetric fusion mechanism to generate dual retrieval queries of different granularity, enabling the model to fully use multi-modal information. Specifically, we propose a novel method termed Dual Retrieval Queries Fine-Tuning for Composed Image Retrieval (DRQ-CIR), which consists of two key components: 1) a Bilateral Multi-Modal Fusion (BMMF) module based on pre-trained VLMs, which combines the reference image and modification text to generate an enriched retrieval query; and 2) a Dual Retrieval Queries Fine-Tuning (DRQ-FT) module, which employs latent prompts to generate an enhanced retrieval query. Dual retrieval queries are used for contrastive learning with the target image to fine-tune the model and improve retrieval performance. Additionally, we introduce a bi-directional training paradigm to ensure retrieval consistency and further exploit the triplets. Extensive experiments validate the effectiveness of our proposed method on four established benchmark datasets. (Code will be available at: https://github.com/Crystal-twy998/DRQ-CIR).