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Image inpainting is the task of filling in missing or masked region of animage with semantically meaningful contents. Recent methods have shownsignificant improvement in dealing with large-scale missing regions. However,these methods usually require large training datasets to achieve satisfactoryresults and there has been limited research into training these models on asmall number of samples. To address this, we present a novel few-shotgenerative residual image inpainting method that produces high-qualityinpainting results. The core idea is to propose an iterative residual reasoningmethod that incorporates Convolutional Neural Networks (CNNs) for featureextraction and Transformers for global reasoning within generative adversarialnetworks, along with image-level and patch-level discriminators. We alsopropose a novel forgery-patch adversarial training strategy to create faithfultextures and detailed appearances. Extensive evaluations show that our methodoutperforms previous methods on the few-shot image inpainting task, bothquantitatively and qualitatively.

GRIG: Few-Shot Generative Residual Image Inpainting