Semi-supervised learning is a challenging problem which aims to construct a model by learning from limited labeled examples. Numerous methods for this task focus on utilizing the predictions of unlabeled instances consistency alone to regularize networks. However, treating labeled and unlabeled data separately often leads to the discarding of mass prior knowledge learned from the labeled examples. %, and failure to mine the feature interaction between the labeled and unlabeled image pairs. In this paper, we propose a novel method for semi-supervised semantic segmentation named GuidedMix-Net, by leveraging labeled information to guide the learning of unlabeled instances. Specifically, GuidedMix-Net employs three operations: 1) interpolation of similar labeled-unlabeled image pairs; 2) transfer of mutual information; 3) generalization of pseudo masks. It enables segmentation models can learning the higher-quality pseudo masks of unlabeled data by transfer the knowledge from labeled samples to unlabeled data. Along with supervised learning for labeled data, the prediction of unlabeled data is jointly learned with the generated pseudo masks from the mixed data. Extensive experiments on PASCAL VOC 2012, and Cityscapes demonstrate the effectiveness of our GuidedMix-Net, which achieves competitive segmentation accuracy and significantly improves the mIoU by +7$\%$ compared to previous approaches.
翻译:半监督的学习是一个具有挑战性的问题,目的是通过从有限标签实例中学习来构建一个模型。本任务的许多方法侧重于仅仅利用未贴标签实例一致性的预测来规范网络。然而,单独处理标签和未贴标签数据往往导致丢弃从标签实例中获取的大规模先前知识。%,以及未将标签和未贴标签图像配对之间的特征互动埋设。在本文件中,我们提出了一个名为“方向Mix-Net”的半监督语义分解新颖方法,通过利用标签信息指导未贴标签实例的学习。具体地说,指导Mix-Net采用三种操作:1) 类贴标签和未贴标签的图像配对的内插图;2) 互换信息;3) 假面罩的概括化。它使分解模型能够通过将标签样本中的知识转让给未贴标签数据,从而学习质量更高的伪口罩。除了对标签数据进行监管学习外,对未贴标签数据进行预测与生成的伪面面面面面面罩一起学习,从混合数据中生成的$;7) 相互交换信息;通过大规模实验,使PASAL+VOC部分得以实现。