Multimodal Deep Neural Network

Multimodal deep neural networks integrate data from multiple sources (e.g., images, text, audio) to improve the accuracy and interpretability of machine learning models. Current research focuses on optimizing model architectures, such as incorporating attention mechanisms and Siamese networks, to effectively fuse diverse data modalities and address challenges like imbalanced datasets and resource constraints for deployment on edge devices. These advancements are impacting diverse fields, enabling improved applications in areas ranging from personality analysis and disease severity prediction to visual question answering on resource-limited hardware. The development of more efficient and interpretable multimodal models is a key area of ongoing investigation.