  Multi-scale representations are crucial for semantic segmentation. The
community has witnessed the flourish of semantic segmentation convolutional
neural networks (CNN) exploiting multi-scale contextual information. Motivated
by that the vision transformer (ViT) is powerful in image classification, some
semantic segmentation ViTs are recently proposed, most of them attaining
impressive results but at a cost of computational economy. In this paper, we
succeed in introducing multi-scale representations into semantic segmentation
ViT via window attention mechanism and further improves the performance and
efficiency. To this end, we introduce large window attention which allows the
local window to query a larger area of context window at only a little
computation overhead. By regulating the ratio of the context area to the query
area, we enable the $\textit{large window attention}$ to capture the contextual
information at multiple scales. Moreover, the framework of spatial pyramid
pooling is adopted to collaborate with $\textit{the large window attention}$,
which presents a novel decoder named $\textbf{la}$rge $\textbf{win}$dow
attention spatial pyramid pooling (LawinASPP) for semantic segmentation ViT.
Our resulting ViT, Lawin Transformer, is composed of an efficient hierachical
vision transformer (HVT) as encoder and a LawinASPP as decoder. The empirical
results demonstrate that Lawin Transformer offers an improved efficiency
compared to the existing method. Lawin Transformer further sets new
state-of-the-art performance on Cityscapes (84.4% mIoU), ADE20K (56.2% mIoU)
and COCO-Stuff datasets. The code will be released at
https://github.com/yan-hao-tian/lawin
