This paper presents Hire-MLP, a simple yet competitive vision MLP architecture via hierarchical rearrangement. Previous vision MLPs like MLP-Mixer are not flexible for various image sizes and are inefficient to capture spatial information by flattening the tokens. Hire-MLP innovates the existing MLP-based models by proposing the idea of hierarchical rearrangement to aggregate the local and global spatial information while being versatile for downstream tasks. Specifically, the inner-region rearrangement is designed to capture local information inside a spatial region. Moreover, to enable information communication between different regions and capture global context, the cross-region rearrangement is proposed to circularly shift all tokens along spatial directions. The proposed Hire-MLP architecture is built with simple channel-mixing MLPs and rearrangement operations, thus enjoys high flexibility and inference speed. Experiments show that our Hire-MLP achieves state-of-the-art performance on the ImageNet-1K benchmark. In particular, Hire-MLP achieves an 83.4% top-1 accuracy on ImageNet, which surpasses previous Transformer-based and MLP-based models with better trade-off for accuracy and throughput.
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