SpiralMLP: A Lightweight Vision MLP Architecture

Haojie Mu, Burhan Ul Tayyab, Nicholas Chua; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 8616-8626

We present SpiralMLP a novel architecture 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 enabling 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.