论文标题
标签驱动的重建语义分割中域适应的重建
Label-Driven Reconstruction for Domain Adaptation in Semantic Segmentation
论文作者
论文摘要
无监督的域的适应性使语义分割中的像素注释的需求。最常见的策略之一是将图像从源域转换为目标域，然后使用对抗性学习将其边际分布对齐。但是，由于源域的主要数据大小，源对目标翻译扩大了翻译图像中的偏差并引入了额外的计算。此外，无法通过全局特征对齐来确保源和目标域的联合分布的一致性。在这里，我们提出了一个创新的框架，旨在减轻图像翻译偏置和与同一类别相结合的跨域功能。这是通过1）执行目标 - 源翻译的方法来实现的，2）从其预测标签中重建源和目标图像。从合成到真正的城市场景理解的广泛实验表明，我们的框架与现有的最新方法竞争。
Unsupervised domain adaptation enables to alleviate the need for pixel-wise annotation in the semantic segmentation. One of the most common strategies is to translate images from the source domain to the target domain and then align their marginal distributions in the feature space using adversarial learning. However, source-to-target translation enlarges the bias in translated images and introduces extra computations, owing to the dominant data size of the source domain. Furthermore, consistency of the joint distribution in source and target domains cannot be guaranteed through global feature alignment. Here, we present an innovative framework, designed to mitigate the image translation bias and align cross-domain features with the same category. This is achieved by 1) performing the target-to-source translation and 2) reconstructing both source and target images from their predicted labels. Extensive experiments on adapting from synthetic to real urban scene understanding demonstrate that our framework competes favorably against existing state-of-the-art methods.
