Image editing is one of the most popular directions in computer vision. Recently, many methods have benefited from the advances in deep learning, showing promising performance in the image editing task by inpainting the editing areas. These methods take advantage of edge information as user guidance to generate the desired content. However, they are suffering from generating color discrepancy and inconsistent boundaries. In this letter, we propose a deep image editing method based on a self-attention network which copies information for each of the small patches from distant spatial locations. The proposed method smooths the image, computes segmentation maps, and utilizes the segmentation information for guiding the self-attention layers to explicitly leverage image features from surrounding areas with similar appearances. Experimental results show that the proposed method achieves better performance, is flexible for different purposes, and is fast for implementation.

中文翻译：

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通过分割引导的自注意力网络进行图像编辑

图像编辑是计算机视觉中最流行的方向之一。最近，许多方法受益于深度学习的进步，通过修复编辑区域在图像编辑任务中显示出良好的性能。这些方法利用边缘信息作为用户指南来生成所需的内容。但是，它们会产生颜色差异和不一致的边界。在这封信中，我们提出了一种基于自注意力网络的深度图像编辑方法，该方法从远处的空间位置复制每个小块的信息。所提出的方法平滑图像，计算分割图，并利用分割信息引导自注意力层明确利用来自具有相似外观的周围区域的图像特征。