泛光分割統一了語義分割和執行個體分割，近年來越來越受到人們的關注。然而，大多數現有研究是在受監督的學習設定下進行的，而在不同任務和應用中至關重要的不受監督的域自适應泛光分割在很大程度上被忽視。我們設計了一個域自适應泛光分割網絡，利用跨式一緻性和任務間規範化，實作最佳域自适應泛光分割。不同風格的一緻性利用不同風格的相同圖像的幾何變化，這些圖像建構了某些自我監督，引導網絡學習域變特征。任務間規範化利用執行個體分割和語義分割的互補性，并将其用作跨域更好地功能對齊的限制。對多個領域自适應泛光分割任務（例如，合成到真實和真實到現實）的廣泛實驗表明，與最先進的網絡相比，我們提議的網絡實作了卓越的細分性能。
标題原文：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