Temporal Mamba

Temporal Mamba refers to a family of state-space models used in various applications to efficiently capture long-range temporal dependencies in sequential data, overcoming limitations of traditional methods like CNNs and Transformers. Current research focuses on adapting Mamba architectures for diverse tasks, including physiological signal extraction from videos, crop classification from satellite imagery, and medical image segmentation, often incorporating them into hybrid models with CNNs or other components to leverage complementary strengths. This approach offers significant advantages in handling long sequences and high-dimensional data, leading to improved accuracy and efficiency across a range of scientific and engineering domains.