Benefiting from powerful convolutional neural networks (CNNs), learning-based
image inpainting methods have made significant breakthroughs over the years.
However, some nature of CNNs (e.g. local prior, spatially shared parameters)
limit the performance in the face of broken images with diverse and complex
forms. Recently, a class of attention-based network architectures, called
transformer, has shown significant performance on natural language processing
fields and high-level vision tasks. Compared with CNNs, attention operators are
better at long-range modeling and have dynamic weights, but their computational
complexity is quadratic in spatial resolution, and thus less suitable for
applications involving higher resolution images, such as image inpainting. In
this paper, we design a novel attention linearly related to the resolution
according to Taylor expansion. And based on this attention, a network called
$T$-former is designed for image inpainting. Experiments on several benchmark
datasets demonstrate that our proposed method achieves state-of-the-art
accuracy while maintaining a relatively low number of parameters and
computational complexity. The code can be found at
\href{this https URL}{github.com/dengyecode/T-former\_image\_inpainting}

本文中，我们设计了一种新的基于注意力的线性结构，称为 T-former，用于图像修复，实验表明该方法在保持较低的参数数量和计算复杂度的同时，实现了最先进的精度。