Spectral Token

Spectral tokens represent a novel approach to data processing in various machine learning tasks, aiming to improve efficiency and performance by encoding spectral or frequency-domain information directly into transformer architectures. Current research focuses on developing efficient token generation and merging methods within models like Mamba and Supertoken Transformers, often incorporating techniques such as morphological operations, multi-head self-attention, and dynamic supertoken optimization to handle high-dimensional data like hyperspectral images and LiDAR point clouds. This approach shows promise for enhancing the speed and accuracy of tasks ranging from image and video classification to solving partial differential equations, offering significant advantages over traditional methods in terms of computational cost and performance.