论文标题
图像和分割掩模的一声合成
One-Shot Synthesis of Images and Segmentation Masks
论文作者
论文摘要
与生成对抗网络（GAN）的图像和分割掩模的联合合成有望减少用像素式注释收集图像数据所需的精力。但是，要学习高保真图像掩码综合，现有的GAN方法首先需要一个需要大量图像数据的预训练阶段，这限制了其在受限图像域中的利用。在这项工作中，我们采取了一个步骤来减少此限制，并介绍了一次性图像掩码合成的任务。我们旨在仅给出一个单个标记的示例，生成各种图像及其分割面具，并假设与以前的模型相反，无法访问任何预训练数据。为此，我们受到了单图像gan的最新体系结构的发展的启发，我们介绍了OSMIS模型，该模型可以合成分割掩模，这些掩模与单光制制度中生成的图像完全一致。除了实现产生的口罩的高保真度外，OSMIS在图像合成质量和多样性中的最先进的单图像模型优于最先进的单位图。此外，尽管没有使用任何其他数据，但OSMIS表现出令人印象深刻的能力，可以作为一次性分割应用程序的有用数据增强来源，从而提供了与标准数据增强技术相辅相成的性能提高。代码可从https://github.com/ boschresearch/One-shot-synsation获得
Joint synthesis of images and segmentation masks with generative adversarial networks (GANs) is promising to reduce the effort needed for collecting image data with pixel-wise annotations. However, to learn high-fidelity image-mask synthesis, existing GAN approaches first need a pre-training phase requiring large amounts of image data, which limits their utilization in restricted image domains. In this work, we take a step to reduce this limitation, introducing the task of one-shot image-mask synthesis. We aim to generate diverse images and their segmentation masks given only a single labelled example, and assuming, contrary to previous models, no access to any pre-training data. To this end, inspired by the recent architectural developments of single-image GANs, we introduce our OSMIS model which enables the synthesis of segmentation masks that are precisely aligned to the generated images in the one-shot regime. Besides achieving the high fidelity of generated masks, OSMIS outperforms state-of-the-art single-image GAN models in image synthesis quality and diversity. In addition, despite not using any additional data, OSMIS demonstrates an impressive ability to serve as a source of useful data augmentation for one-shot segmentation applications, providing performance gains that are complementary to standard data augmentation techniques. Code is available at https://github.com/ boschresearch/one-shot-synthesis
