  In unsupervised domain adaptation (UDA), directly adapting from the source to
the target domain usually suffers significant discrepancies and leads to
insufficient alignment. Thus, many UDA works attempt to vanish the domain gap
gradually and softly via various intermediate spaces, dubbed domain bridging
(DB). However, for dense prediction tasks such as domain adaptive semantic
segmentation (DASS), existing solutions have mostly relied on rough style
transfer and how to elegantly bridge domains is still under-explored. In this
work, we resort to data mixing to establish a deliberated domain bridging (DDB)
for DASS, through which the joint distributions of source and target domains
are aligned and interacted with each in the intermediate space. At the heart of
DDB lies a dual-path domain bridging step for generating two intermediate
domains using the coarse-wise and the fine-wise data mixing techniques,
alongside a cross-path knowledge distillation step for taking two complementary
models trained on generated intermediate samples as 'teachers' to develop a
superior 'student' in a multi-teacher distillation manner. These two
optimization steps work in an alternating way and reinforce each other to give
rise to DDB with strong adaptation power. Extensive experiments on adaptive
segmentation tasks with different settings demonstrate that our DDB
significantly outperforms state-of-the-art methods. Code is available at
https://github.com/xiaoachen98/DDB.git.
