  Homography estimation is a basic computer vision task, which aims to obtain
the transformation from multi-view images for image alignment. Unsupervised
learning homography estimation trains a convolution neural network for feature
extraction and transformation matrix regression. While the state-of-theart
homography method is based on convolution neural networks, few work focuses on
transformer which shows superiority in highlevel vision tasks. In this paper,
we propose a strong-baseline model based on the Swin Transformer, which
combines convolution neural network for local features and transformer module
for global features. Moreover, a cross non-local layer is introduced to search
the matched features within the feature maps coarsely. In the homography
regression stage, we adopt an attention layer for the channels of correlation
volume, which can drop out some weak correlation feature points. The experiment
shows that in 8 Degree-of-Freedoms(DOFs) homography estimation our method
overperforms the state-of-the-art method.
