  This paper studies a practical domain adaptive (DA) semantic segmentation
problem where only pseudo-labeled target data is accessible through a black-box
model. Due to the domain gap and label shift between two domains,
pseudo-labeled target data contains mixed closed-set and open-set label noises.
In this paper, we propose a simplex noise transition matrix (SimT) to model the
mixed noise distributions in DA semantic segmentation and formulate the problem
as estimation of SimT. By exploiting computational geometry analysis and
properties of segmentation, we design three complementary regularizers, i.e.
volume regularization, anchor guidance, convex guarantee, to approximate the
true SimT. Specifically, volume regularization minimizes the volume of simplex
formed by rows of the non-square SimT, which ensures outputs of segmentation
model to fit into the ground truth label distribution. To compensate for the
lack of open-set knowledge, anchor guidance and convex guarantee are devised to
facilitate the modeling of open-set noise distribution and enhance the
discriminative feature learning among closed-set and open-set classes. The
estimated SimT is further utilized to correct noise issues in pseudo labels and
promote the generalization ability of segmentation model on target domain data.
Extensive experimental results demonstrate that the proposed SimT can be
flexibly plugged into existing DA methods to boost the performance. The source
code is available at https://github.com/CityU-AIM-Group/SimT.
