With autonomous industries on the rise, domain adaptation of the visual
perception stack is an important research direction due to the cost savings
promise. Much prior art was dedicated to domain-adaptive semantic segmentation
in the synthetic-to-real context. Despite being a crucial output of the
perception stack, panoptic segmentation has been largely overlooked by the
domain adaptation community. Therefore, we revisit well-performing domain
adaptation strategies from other fields, adapt them to panoptic segmentation,
and show that they can effectively enhance panoptic domain adaptation. Further,
we study the panoptic network design and propose a novel architecture (EDAPS)
designed explicitly for domain-adaptive panoptic segmentation. It uses a
shared, domain-robust transformer encoder to facilitate the joint adaptation of
semantic and instance features, but task-specific decoders tailored for the
specific requirements of both domain-adaptive semantic and instance
segmentation. As a result, the performance gap seen in challenging panoptic
benchmarks is substantially narrowed. EDAPS significantly improves the
state-of-the-art performance for panoptic segmentation UDA by a large margin of
25% on SYNTHIA-to-Cityscapes and even 72% on the more challenging
SYNTHIA-to-Mapillary Vistas. The implementation is available at
this https URL

本文介绍了一种全新的网络架构 EDAPS，它使用共享的、domain-robust 转换器编码器，以方便语义特征和实例特征的联合适应，但还针对域适应的语义和实例分割任务的特定要求设计了任务特定的解码器，从而显著提高了挑战性 panoptic 基准测试的性能。