ARIF: An Adaptive Attention-Based Cross-Modal Representation Integration Framework

Representation alignment and fusion are key tasks in cross-modal data integration, facing various challenges such as mismatched feature dimensions, inconsistencies in feature spaces, and constraints on downstream tasks. To address these issues, we propose an Adaptive Attention-based Cross-Modal Representation Integration Framework. This framework can adaptively capture and associate feature information from different modalities and effectively align them to obtain a unified representation of cross-modal information in a common feature space. Additionally, ARIF enhances the overall fusion representation by selectively incorporating modality-specific information to adapt to accommodate different downstream tasks. We conduct extensive experiments on three benchmark tasks: Classification, Regression, and Image Generation. The results indicate that the ARIF framework demonstrates significant potential for effectively integrating cross-modal data.