The increasing relevance of panoptic segmentation is tied to the advancements
in autonomous driving and AR/VR applications. However, the deployment of such
models has been limited due to the expensive nature of dense data annotation,
giving rise to unsupervised domain adaptation (UDA). A key challenge in
panoptic UDA is reducing the domain gap between a labeled source and an
unlabeled target domain while harmonizing the subtasks of semantic and instance
segmentation to limit catastrophic interference. While considerable progress
has been achieved, existing approaches mainly focus on the adaptation of
semantic segmentation. In this work, we focus on incorporating instance-level
adaptation via a novel instance-aware cross-domain mixing strategy IMix. IMix
significantly enhances the panoptic quality by improving instance segmentation
performance. Specifically, we propose inserting high-confidence predicted
instances from the target domain onto source images, retaining the
exhaustiveness of the resulting pseudo-labels while reducing the injected
confirmation bias. Nevertheless, such an enhancement comes at the cost of
degraded semantic performance, attributed to catastrophic forgetting. To
mitigate this issue, we regularize our semantic branch by employing CLIP-based
domain alignment (CDA), exploiting the domain-robustness of natural language
prompts. Finally, we present an end-to-end model incorporating these two
mechanisms called LIDAPS, achieving state-of-the-art results on all popular
panoptic UDA benchmarks.

提出了一种名为 LIDAPS 的新型模型，通过 IMix 策略实现了实例级的域适应，提高了全景质量，并利用 CLIP-based domain alignment (CDA) 方法来解决语义性能下降的问题，在流行的全景无监督域适应基准测试中取得了最先进的结果。