Hierarchical Fusion

Hierarchical fusion is a technique in machine learning that combines information from multiple sources or modalities in a layered manner, aiming to improve the accuracy and robustness of models compared to simpler fusion methods. Current research focuses on developing efficient and effective hierarchical architectures, often employing transformers, attention mechanisms, and generative models, to integrate diverse data types such as images, LiDAR, text, and sensor readings. This approach is proving valuable across various applications, including medical image analysis, autonomous driving, and speech recognition, by leveraging the complementary strengths of different data sources to achieve superior performance in complex tasks.