SimViT: Exploring a Simple Vision Transformer with sliding windows

• URL: http://arxiv.org/abs/2112.13085v1
• Date: Fri, 24 Dec 2021 15:18:20 GMT
• Title: SimViT: Exploring a Simple Vision Transformer with sliding windows
• Authors: Gang Li, Di Xu, Xing Cheng, Lingyu Si, Changwen Zheng
• Abstract summary: We introduce a vision Transformer named SimViT, to incorporate spatial structure and local information into the vision Transformers. SimViT extracts multi-scale hierarchical features from different layers for dense prediction tasks. Our SimViT-Micro only needs 3.3M parameters to achieve 71.1% top-1 accuracy on ImageNet-1k dataset.
• Score: 3.3107339588116123
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Although vision Transformers have achieved excellent performance as backbone models in many vision tasks, most of them intend to capture global relations of all tokens in an image or a window, which disrupts the inherent spatial and local correlations between patches in 2D structure. In this paper, we introduce a simple vision Transformer named SimViT, to incorporate spatial structure and local information into the vision Transformers. Specifically, we introduce Multi-head Central Self-Attention(MCSA) instead of conventional Multi-head Self-Attention to capture highly local relations. The introduction of sliding windows facilitates the capture of spatial structure. Meanwhile, SimViT extracts multi-scale hierarchical features from different layers for dense prediction tasks. Extensive experiments show the SimViT is effective and efficient as a general-purpose backbone model for various image processing tasks. Especially, our SimViT-Micro only needs 3.3M parameters to achieve 71.1% top-1 accuracy on ImageNet-1k dataset, which is the smallest size vision Transformer model by now. Our code will be available in https://github.com/ucasligang/SimViT.

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