Domain adaptive panoptic segmentation aims to mitigate data annotation
challenge by leveraging off-the-shelf annotated data in one or multiple related
source domains. However, existing studies employ two separate networks for
instance segmentation and semantic segmentation which lead to excessive network
parameters as well as complicated and computationally intensive training and
inference processes. We design UniDAformer, a unified domain adaptive panoptic
segmentation transformer that is simple but can achieve domain adaptive
instance segmentation and semantic segmentation simultaneously within a single
network. UniDAformer introduces Hierarchical Mask Calibration (HMC) that
rectifies inaccurate predictions at the level of regions, superpixels and
pixels via online self-training on the fly. It has three unique features: 1) it
enables unified domain adaptive panoptic adaptation; 2) it mitigates false
predictions and improves domain adaptive panoptic segmentation effectively; 3)
it is end-to-end trainable with a much simpler training and inference pipeline.
Extensive experiments over multiple public benchmarks show that UniDAformer
achieves superior domain adaptive panoptic segmentation as compared with the
state-of-the-art.

UniDAformer 是一种简单的统一领域自适应全景分割变压器，采用分层掩模校准来纠正特定区域的不准确预测，可以同时实现领域自适应实例分割和语义分割。它可用于统一领域自适应全景适应，并通过在线自训练来缓解错误预测，有效地提高了领域自适应全景分割的效果，训练及推理流程简单，经过多项公共基准测试，UniDAformer 的领域自适应全景分割优于现有技术水平。