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arXiv · 2107.04735

Local-to-Global Self-Attention in Vision Transformers

Jinpeng Li·Yichao Yan·Shengcai Liao·Xiaokang Yang·Ling Shao
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Abstract

Transformers have demonstrated great potential in computer vision tasks. To avoid dense computations of self-attentions in high-resolution visual data, some recent Transformer models adopt a hierarchical design, where self-attentions are only computed within local windows. This design significantly improves the efficiency but lacks global feature reasoning in early stages. In this work, we design a multi-path structure of the Transformer, which enables local-to-global reasoning at multiple granularities in each stage. The proposed framework is computationally efficient and highly effective. With a marginal increasement in computational overhead, our model achieves notable improvements in both image classification and semantic segmentation. Code is available at https://github.com/ljpadam/LG-Transformer

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Jinpeng Li, Yichao Yan, Shengcai Liao, Xiaokang Yang, Ling Shao. 2021-07-10. Local-to-Global Self-Attention in Vision Transformers. https://arxiv.org/abs/2107.04735

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