Progressive Fusion Mamba

Progressive Fusion Mamba refers to a family of state-space models designed for efficient and effective multimodal data fusion, particularly in scenarios with long sequences or high-dimensional data. Current research focuses on applying Mamba architectures to diverse tasks, including depression detection from audio-visual data, robot localization using text and point clouds, and robust RGB-T tracking, often incorporating attention mechanisms for improved feature alignment and interaction. These models offer a compelling alternative to transformers, providing comparable performance with linear complexity, thereby enabling the processing of significantly larger datasets and longer sequences across various applications in computer vision, natural language processing, and biomedical image analysis.