A Modular Framework for Robust Multimodal Representation Learning via Dynamic Modality Weighting

Abstract

 Multimodal systems empower machines to interpret and reason over diverse information sources such as text, images, and audio, thereby achieving a level of understanding closer to human cognition. This paper introduces a unified framework that combines modality-specific encoders, a hierarchical cross-modal fusion module, and dynamic weighting strategies. We validate the framework on three representative tasks-emotion recognition, image-text retrieval, and medical report generation—where it consistently outperforms competitive baselines in both accuracy and robustness. Comprehensive experiments and case analyses highlight its adaptability to real-world scenarios. The proposed solution is scalable, interpretable, and broadly applicable to fields such as healthcare, education, and human-computer interaction.