  Source-free domain adaptation has developed rapidly in recent years, where
the well-trained source model is adapted to the target domain instead of the
source data, offering the potential for privacy concerns and intellectual
property protection. However, a number of feature alignment techniques in prior
domain adaptation methods are not feasible in this challenging problem setting.
Thereby, we resort to probing inherent domain-invariant feature learning and
propose a curriculum-style self-training approach for source-free domain
adaptive semantic segmentation. In particular, we introduce a curriculum-style
entropy minimization method to explore the implicit knowledge from the source
model, which fits the trained source model to the target data using certain
information from easy-to-hard predictions. We then train the segmentation
network by the proposed complementary curriculum-style self-training, which
utilizes the negative and positive pseudo labels following the
curriculum-learning manner. Although negative pseudo-labels with high
uncertainty cannot be identified with the correct labels, they can definitely
indicate absent classes. Moreover, we employ an information propagation scheme
to further reduce the intra-domain discrepancy within the target domain, which
could act as a standard post-processing method for the domain adaptation field.
Furthermore, we extend the proposed method to a more challenging black-box
source model scenario where only the source model's predictions are available.
Extensive experiments validate that our method yields state-of-the-art
performance on source-free semantic segmentation tasks for both
synthetic-to-real and adverse conditions datasets. The code and corresponding
trained models are released at \url{https://github.com/yxiwang/ATP}.
