Swin-Free: Achieving Better Cross-Window Attention and Efficiency with
  Size-varying Window
Jinkyu Koo John Yang Le An Gwenaelle Cunha Sergio Su Inn Park
Transformer models have shown great potential in computer vision, following
their success in language tasks. Swin Transformer is one of them that
outperforms convolution-based architectures in terms of accuracy, while
improving efficiency when compared to Vision Transformer (ViT) and its
variants, which have quadratic complexity with respect to the input size. Swin
Transformer features shifting windows that allows cross-window connection while
limiting self-attention computation to non-overlapping local windows. However,
shifting windows introduces memory copy operations, which account for a
significant portion of its runtime. To mitigate this issue, we propose
Swin-Free in which we apply size-varying windows across stages, instead of
shifting windows, to achieve cross-connection among local windows. With this
simple design change, Swin-Free runs faster than the Swin Transformer at
inference with better accuracy. Furthermore, we also propose a few of Swin-Free
variants that are faster than their Swin Transformer counterparts.