泛光分割统一了语义分割和实例分割，近年来越来越受到人们的关注。然而，大多数现有研究是在受监督的学习设置下进行的，而在不同任务和应用中至关重要的不受监督的域自适应泛光分割在很大程度上被忽视。我们设计了一个域自适应泛光分割网络，利用跨式一致性和任务间规范化，实现最佳域自适应泛光分割。不同风格的一致性利用不同风格的相同图像的几何变化，这些图像构建了某些自我监督，引导网络学习域变特征。任务间规范化利用实例分割和语义分割的互补性，并将其用作跨域更好地功能对齐的约束。对多个领域自适应泛光分割任务（例如，合成到真实和真实到现实）的广泛实验表明，与最先进的网络相比，我们提议的网络实现了卓越的细分性能。
标题原文：Cross-View Regularization for Domain Adaptive Panoptic Segmentation
原文：Panoptic segmentation unifies semantic segmentation and instance segmentation which has been attracting increasing attention in recent years. However, most existing research was conducted under a supervised learning setup whereas unsupervised domain adaptive panoptic segmentation which is critical in different tasks and applications is largely neglected. We design a domain adaptive panoptic segmentation network that exploits inter-style consistency and inter-task regularization for optimal domain adaptive panoptic segmentation. The inter-style consistency leverages geometric invariance across the same image of the different styles which fabricates certain self-supervisions to guide the network to learn domain-invariant features. The inter-task regularization exploits the complementary nature of instance segmentation and semantic segmentation and uses it as a constraint for better feature alignment across domains. Extensive experiments over multiple domain adaptive panoptic segmentation tasks (e.g., synthetic-to-real and real-to-real) show that our proposed network achieves superior segmentation performance as compared with the state-of-the-art.
2103.02584.pdf