Multimodal Architecture

Multimodal architectures aim to integrate information from diverse data sources (e.g., text, images, audio) for improved performance in tasks like sentiment analysis, object detection, and medical diagnosis. Current research emphasizes efficient fusion methods, including early fusion at the input stage and deeper fusion within model layers, with a focus on optimizing architectures for varying input resolutions and lengths, as well as addressing modality imbalance issues. These advancements are driving improvements in accuracy and robustness across various applications, from automated depression classification to accident prediction and gravitational lensing analysis, highlighting the growing importance of multimodal approaches in artificial intelligence.