  Recent advances in unsupervised domain adaptation have seen considerable
progress in semantic segmentation. Existing methods either align different
domains with adversarial training or involve the self-learning that utilizes
pseudo labels to conduct supervised training. The former always suffers from
the unstable training caused by adversarial training and only focuses on the
inter-domain gap that ignores intra-domain knowledge. The latter tends to put
overconfident label prediction on wrong categories, which propagates errors to
more samples. To solve these problems, we propose a two-stage adaptive semantic
segmentation method based on the local Lipschitz constraint that satisfies both
domain alignment and domain-specific exploration under a unified principle. In
the first stage, we propose the local Lipschitzness regularization as the
objective function to align different domains by exploiting intra-domain
knowledge, which explores a promising direction for non-adversarial adaptive
semantic segmentation. In the second stage, we use the local Lipschitzness
regularization to estimate the probability of satisfying Lipschitzness for each
pixel, and then dynamically sets the threshold of pseudo labels to conduct
self-learning. Such dynamical self-learning effectively avoids the error
propagation caused by noisy labels. Optimization in both stages is based on the
same principle, i.e., the local Lipschitz constraint, so that the knowledge
learned in the first stage can be maintained in the second stage. Further, due
to the model-agnostic property, our method can easily adapt to any CNN-based
semantic segmentation networks. Experimental results demonstrate the excellent
performance of our method on standard benchmarks.
