One-shot semantic segmentation aims to recognize unseen object regions by using the reference of only one annotated example. Many deep convolutional neural networks have achieved enormous success on this task. However, most of the existing methods only use a fixed annotated dataset to train the network. The remaining unannotated examples remain difficult to be leveraged and recognized. In this study, we propose a procedure based on the generative adversarial network to enable the one-shot semantic segmentation model for learning information from previously unknown categories. Our method contains a segmentation network that generates segmentation predictions. We then use a discriminator to differentiate the probability maps of segmentation prediction from the ground truth distribution. Consequently, we can ignore the pixels classified as fake and only use trustworthy regions as the label to train the segmentation network, thus achieving semi-supervised learning. Experimental results demonstrate the effectiveness of the proposed adversarial learning method with an average gain of 49.7% accuracy score on the PASCAL VOC 2012 dataset.

一键式语义分割旨在通过仅使用一个带注释的示例的引用来识别看不见的对象区域。许多深度卷积神经网络在此任务上均取得了巨大成功。但是，大多数现有方法仅使用固定的带注释的数据集来训练网络。其余未注释的示例仍然很难被利用和识别。在这项研究中，我们提出了一个基于生成对抗网络的程序，以使单次语义分割模型能够从以前未知的类别中学习信息。我们的方法包含一个生成细分预测的细分网络。然后，我们使用判别器将细分预测的概率图与地面真实分布区分开来。所以，我们可以忽略分类为假像素的像素，仅使用可信赖区域作为标签来训练分割网络，从而实现半监督学习。实验结果证明了所提出的对抗学习方法的有效性，PASCAL VOC 2012数据集的平均准确率得分为49.7％。