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Despite their simpler information fusion designs compared with VisionTransformers and Convolutional Neural Networks, Vision MLP architectures havedemonstrated strong performance and high data efficiency in recent research.However, existing works such as CycleMLP and Vision Permutator typically modelspatial information in equal-size spatial regions and do not considercross-scale spatial interactions. Further, their token mixers only model 1- or2-axis correlations, avoiding 3-axis spatial-channel mixing due to itscomputational demands. We therefore propose CS-Mixer, a hierarchical Vision MLPthat learns dynamic low-rank transformations for spatial-channel mixing throughcross-scale local and global aggregation. The proposed methodology achievescompetitive results on popular image recognition benchmarks without incurringsubstantially more compute. Our largest model, CS-Mixer-L, reaches 83.2% top-1accuracy on ImageNet-1k with 13.7 GFLOPs and 94 M parameters.

CS-Mixer: A Cross-Scale Vision MLP Model with Spatial-Channel Mixing