Cross Modal Fusion Network

Cross-modal fusion networks aim to improve performance by integrating information from multiple data sources, such as images, text, and audio, leveraging the complementary strengths of each modality. Current research focuses on developing effective fusion strategies within various architectures, including U-Net, Transformer-based models, and those incorporating attention mechanisms to selectively weigh the contribution of different modalities. These advancements have demonstrated improved accuracy and robustness in diverse applications, including medical diagnosis (e.g., predicting pulmonary embolism or stroke prognosis), scene text recognition, and emotion recognition from conversations, highlighting the significant potential of cross-modal fusion for complex data analysis tasks.