  After the great success of Vision Transformer variants (ViTs) in computer
vision, it has also demonstrated great potential in domain adaptive semantic
segmentation. Unfortunately, straightforwardly applying local ViTs in domain
adaptive semantic segmentation does not bring in expected improvement. We find
that the pitfall of local ViTs is due to the severe high-frequency components
generated during both the pseudo-label construction and features alignment for
target domains. These high-frequency components make the training of local ViTs
very unsmooth and hurt their transferability. In this paper, we introduce a
low-pass filtering mechanism, momentum network, to smooth the learning dynamics
of target domain features and pseudo labels. Furthermore, we propose a dynamic
of discrepancy measurement to align the distributions in the source and target
domains via dynamic weights to evaluate the importance of the samples. After
tackling the above issues, extensive experiments on sim2real benchmarks show
that the proposed method outperforms the state-of-the-art methods. Our codes
are available at https://github.com/alpc91/TransDA
