Multimodal Causal Relations Enhanced CLIP for Image-to-Text Retrieval

Traditional image-to-text retrieval models learn joint representations by aligning multimodal features, typically learning the weak correlation between image and text data which can introduce noise during modality alignment. To solve this problem, we propose a Multimodal Causal CLIP (MMC-CLIP) network that integrates causal semantic relationships into CLIP for image-to-text retrieval task. Firstly, we employ the Multimodal Causal Discovery (MCD) method, which models the causal relationships of causal variables in both image and text data to construct the multimodal causal graph. Subsequently, we seamlessly integrate the causal nodes extracted from the multimodal causal graph as learnable prompts within the CLIP model, giving rise to the novel framework of Multimodal Causal CLIP. By integrating causal semantics into CLIP, MMC-CLIP effectively enhances the correlation between causal variables in image and text modalities, leading to an improved alignment capability for multimodal image-text data. We demonstrate the superiority and generalization of the proposed method by outperforming all strong baselines in image-to-text retrieval task on the Flickr30K and MSCOCO datasets.