Multimodal Neural Network

Multimodal neural networks integrate information from multiple data sources (e.g., images, text, audio) to improve performance on tasks like image captioning, music generation, and emotion recognition. Current research emphasizes overcoming challenges such as unimodal bias (over-reliance on a single modality) and developing robust fusion strategies that handle missing or adversarial data, often employing architectures like transformers and graph neural networks for effective data integration. These advancements are significantly impacting various fields, enabling more accurate and reliable predictions in applications ranging from medical diagnosis to demand forecasting.