  Very recently, a variety of vision transformer architectures for dense
prediction tasks have been proposed and they show that the design of spatial
attention is critical to their success in these tasks. In this work, we revisit
the design of the spatial attention and demonstrate that a carefully-devised
yet simple spatial attention mechanism performs favourably against the
state-of-the-art schemes. As a result, we propose two vision transformer
architectures, namely, Twins-PCPVT and Twins-SVT. Our proposed architectures
are highly-efficient and easy to implement, only involving matrix
multiplications that are highly optimized in modern deep learning frameworks.
More importantly, the proposed architectures achieve excellent performance on a
wide range of visual tasks, including image level classification as well as
dense detection and segmentation. The simplicity and strong performance suggest
that our proposed architectures may serve as stronger backbones for many vision
tasks. Our code is released at https://github.com/Meituan-AutoML/Twins .
