Multimodal Feature

Multimodal feature research focuses on integrating information from multiple data sources (e.g., text, images, audio) to create richer, more comprehensive representations for various tasks. Current research emphasizes effective fusion strategies, often employing attention mechanisms, transformers, and graph neural networks to capture inter- and intra-modal relationships, and addressing challenges like modality alignment and handling asynchronous data. This field is significant for improving the accuracy and robustness of applications across diverse domains, including medical diagnosis, emotion recognition, and fake news detection, by leveraging the complementary strengths of different data modalities.