  Image inpainting is the task of filling in missing or masked region of an
image with semantically meaningful contents. Recent methods have shown
significant improvement in dealing with large-scale missing regions. However,
these methods usually require large training datasets to achieve satisfactory
results and there has been limited research into training these models on a
small number of samples. To address this, we present a novel few-shot
generative residual image inpainting method that produces high-quality
inpainting results. The core idea is to propose an iterative residual reasoning
method that incorporates Convolutional Neural Networks (CNNs) for feature
extraction and Transformers for global reasoning within generative adversarial
networks, along with image-level and patch-level discriminators. We also
propose a novel forgery-patch adversarial training strategy to create faithful
textures and detailed appearances. Extensive evaluations show that our method
outperforms previous methods on the few-shot image inpainting task, both
quantitatively and qualitatively.
