论文标题
具有错误定位网络的半监督语义分割
Semi-supervised Semantic Segmentation with Error Localization Network
论文作者
论文摘要
本文研究了半监督语义分割的学习，该学习假设只有一小部分训练图像被标记了，而其他训练图像仍然没有标记。未标记的图像通常被分配给用于训练中的伪标签，但是由于确认偏差对伪标签上的错误，这通常会导致性能降解的风险。我们提出了一种新的方法，该方法可以解决这一伪造标签的慢性问题。我们方法的核心是错误定位网络（ELN），这是一种辅助模块，以图像及其分割预测为输入，并确定其伪标签可能是错误的像素。 ELN使半监督的学习能够通过在训练过程中忽略标签噪声来对不准确的伪标签，并可以自然地与自我训练和对比度学习。此外，我们为ELN引入了一种新的学习策略，该策略模拟了ELN训练期间的合理和多样化的分割错误，以增强其概括。我们的方法对Pascal VOC 2012和CityScapes进行了评估，在每个评估环境中，它都优于所有现有方法。
This paper studies semi-supervised learning of semantic segmentation, which assumes that only a small portion of training images are labeled and the others remain unlabeled. The unlabeled images are usually assigned pseudo labels to be used in training, which however often causes the risk of performance degradation due to the confirmation bias towards errors on the pseudo labels. We present a novel method that resolves this chronic issue of pseudo labeling. At the heart of our method lies error localization network (ELN), an auxiliary module that takes an image and its segmentation prediction as input and identifies pixels whose pseudo labels are likely to be wrong. ELN enables semi-supervised learning to be robust against inaccurate pseudo labels by disregarding label noises during training and can be naturally integrated with self-training and contrastive learning. Moreover, we introduce a new learning strategy for ELN that simulates plausible and diverse segmentation errors during training of ELN to enhance its generalization. Our method is evaluated on PASCAL VOC 2012 and Cityscapes, where it outperforms all existing methods in every evaluation setting.
