Hire-MLP: Vision MLP via Hierarchical Rearrangement

• URL: http://arxiv.org/abs/2108.13341v1
• Date: Mon, 30 Aug 2021 16:11:04 GMT
• Title: Hire-MLP: Vision MLP via Hierarchical Rearrangement
• Authors: Jianyuan Guo, Yehui Tang, Kai Han, Xinghao Chen, Han Wu, Chao Xu, Chang Xu and Yunhe Wang
• Abstract summary: Hire-MLP is a simple yet competitive vision architecture via rearrangement. The proposed Hire-MLP architecture is built with simple channel-mixing 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.
• Score: 58.33383667626998
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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