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.

翻译：图像修复是使用语义有意义的内容填充图像中缺失或掩蔽区域的任务。近年来，相关方法在处理大尺度缺失区域方面取得了显著进展。然而，这些方法通常需要大规模训练数据集才能获得令人满意的结果，且针对少量样本训练此类模型的研究十分有限。为解决这一问题，本文提出了一种新颖的少样本生成式残差图像修复方法，能够生成高质量的修复结果。其核心思想是提出一种迭代式残差推理方法，在生成对抗网络中融合卷积神经网络（CNN）进行特征提取与Transformer进行全局推理，并联合图像级与块级判别器。此外，我们还提出了一种新颖的伪造块对抗训练策略，以生成逼真的纹理与精细外观。广泛评估表明，在少样本图像修复任务中，我们的方法在定量与定性上均优于以往方法。