  The existing deep learning fusion methods mainly concentrate on the
convolutional neural networks, and few attempts are made with transformer.
Meanwhile, the convolutional operation is a content-independent interaction
between the image and convolution kernel, which may lose some important
contexts and further limit fusion performance. Towards this end, we present a
simple and strong fusion baseline for infrared and visible images,
namely\textit{ Residual Swin Transformer Fusion Network}, termed as SwinFuse.
Our SwinFuse includes three parts: the global feature extraction, fusion layer
and feature reconstruction. In particular, we build a fully attentional feature
encoding backbone to model the long-range dependency, which is a pure
transformer network and has a stronger representation ability compared with the
convolutional neural networks. Moreover, we design a novel feature fusion
strategy based on $L_{1}$-norm for sequence matrices, and measure the
corresponding activity levels from row and column vector dimensions, which can
well retain competitive infrared brightness and distinct visible details.
Finally, we testify our SwinFuse with nine state-of-the-art traditional and
deep learning methods on three different datasets through subjective
observations and objective comparisons, and the experimental results manifest
that the proposed SwinFuse obtains surprising fusion performance with strong
generalization ability and competitive computational efficiency. The code will
be available at https://github.com/Zhishe-Wang/SwinFuse.
