Cross-View Regularization for Domain Adaptive Panoptic Segmentation

• URL: http://arxiv.org/abs/2103.02584v1
• Date: Wed, 3 Mar 2021 18:29:23 GMT
• Title: Cross-View Regularization for Domain Adaptive Panoptic Segmentation
• Authors: Jiaxing Huang, Dayan Guan, Aoran Xiao, Shijian Lu
• Abstract summary: We design a domain adaptive panoptic segmentation network that exploits inter-style consistency and inter-task regularization. Our proposed network achieves superior segmentation performance as compared with the state-of-the-art.
• Score: 32.77436219094282
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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