Mamba Based Encoder Decoder

Mamba-based encoder-decoder architectures are emerging as efficient and effective alternatives to convolutional neural networks and transformers in various computer vision tasks. Current research focuses on adapting the Mamba state-space model for diverse applications, including semantic segmentation, motion planning, and medical image analysis, often incorporating modifications like specialized decoder blocks and attention mechanisms to enhance performance. This approach offers advantages in handling long-range dependencies and reducing computational costs, leading to improved accuracy and efficiency in tasks requiring processing of long sequences or high-resolution data. The resulting models show promise for advancing fields like autonomous driving, medical imaging, and remote sensing.