  We present SpiralMLP, a novel architecture that introduces a Spiral FC layer
as a replacement for the conventional Token Mixing approach. Differing from
several existing MLP-based models that primarily emphasize axes, our Spiral FC
layer is designed as a deformable convolution layer with spiral-like offsets.
We further adapt Spiral FC into two variants: Self-Spiral FC and Cross-Spiral
FC, which enable both local and global feature integration seamlessly,
eliminating the need for additional processing steps. To thoroughly investigate
the effectiveness of the spiral-like offsets and validate our design, we
conduct ablation studies and explore optimal configurations. In empirical
tests, SpiralMLP reaches state-of-the-art performance, similar to Transformers,
CNNs, and other MLPs, benchmarking on ImageNet-1k, COCO and ADE20K. SpiralMLP
still maintains linear computational complexity O(HW) and is compatible with
varying input image resolutions. Our study reveals that targeting the full
receptive field is not essential for achieving high performance, instead,
adopting a refined approach offers better results.
