TextBridge: A Text-Centered Framework for Enhanced Multimodal Integration and Retrieval

Despite significant advancements in multimodal pre-training, effectively integrating and using latent semantic information across multiple modalities remains a challenge. In this paper, we introduce TextBridge, a text-centered framework that uses the text modality as a semantic anchor to guide cross-modal integration and alignment. TextBridge employs frozen encoders from state-of-the-art pre-trained models and introduces an innovative modality bridge module that enhances semantic alignment and reduces redundancy among different modal features. The framework also incorporates a multi-projection text feature fusion method, enhancing the alignment and integration of text features from diverse modalities into a cohesive semantic representation. To optimize the integration of multimodal information, we make the text encoder trainable and use a text-centered contrastive loss function to enhance the model's ability to capture complementary information across modalities. Extensive experiments on the M5Product dataset demonstrate that TextBridge significantly outperforms the SCALE model in mean average precision (mAP) and precision (Prec), underscoring its effectiveness in multimodal retrieval tasks.