CSWin Transformer: A General Vision Transformer Backbone with Cross-Shaped Windows

We present CSWin Transformer, an efficient and effective Transformer-based backbone for general-purpose vision tasks. By further pretraining on the larger dataset ImageNet-21K, we achieve 87.5% Top-1 accuracy on ImageNet-1K and high segmentation performance on ADE20K with 55.7 mIoU.

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• 1 A challenging issue in Transformer design is that global self-attention is very expensive to compute whereas local self-attention often limits the field of interactions of each token.
• 2 To address this issue, we develop the Cross-Shaped Window self-attention mechanism for computing self-attention in the horizontal and vertical stripes in parallel that form a cross-shaped window, with each stripe obtained by splitting the input feature into stripes of equal width.
• 3 We provide a mathematical analysis of the effect of the stripe width and vary the stripe width for different layers of the Transformer network which achieves strong modeling capability while limiting the computation cost.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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