AI RESEARCH
CHASM: Cross-frequency Harmonized Axis-Separable Mixing for Spectral Token Operators
arXiv CS.CV
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ArXi:2605.14727v1 Announce Type: new Spectral token mixers based on Fourier transforms provide an efficient way to model global interactions in visual feature maps. Existing designs often either apply filter-wise spectral responses along fixed channel axes, or learn adaptive frequency-indexed channel mixing without explicitly aligning the channel directions used across frequencies. We propose CHASM, a Cross-frequency Harmonized Axis-Separable Mixer, as a structured middle ground.