Spectral Vision Transformer for Efficient Tokenization with Limited Data

ArXi:2605.12026v1 Announce Type: cross We propose a novel spectral vision transformer architecture for efficient tokenization in limited data, with an emphasis on medical imaging. We outline convenient theoretical properties arising from the choice of basis including spatial invariance and optimal signal-to-noise ratio. We show reduced complexity arising from the spectral projection compared to spatial vision transformers.