Fusion Method

Fusion methods combine data from multiple sources to improve performance in various applications, primarily aiming to leverage complementary information and overcome limitations of individual modalities. Current research focuses on developing sophisticated fusion architectures, including transformers, autoencoders, and ResNets, often tailored to specific data types (e.g., LiDAR-camera, RGB-thermal, hyperspectral-multispectral) and tasks (e.g., object detection, image segmentation, emotion recognition). These advancements are significantly impacting fields like autonomous driving, remote sensing, and medical imaging by enhancing accuracy, robustness, and efficiency in data analysis and decision-making.