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Recently Transformer has shown good performance in several vision tasks dueto its powerful modeling capabilities. To reduce the quadratic complexitycaused by the attention, some outstanding work restricts attention to localregions or extends axial interactions. However, these methos often lack theinteraction of local and global information, balancing coarse and fine-grainedinformation. To address this problem, we propose AxWin Attention, which modelscontext information in both local windows and axial views. Based on the AxWinAttention, we develop a context-aware vision transformer backbone, named AxWinTransformer, which outperforming the state-of-the-art methods in bothclassification and downstream segmentation and detection tasks.

AxWin Transformer: A Context-Aware Vision Transformer Backbone with Axial Windows