  In semantic segmentation, we aim to train a pixel-level classifier to assign
category labels to all pixels in an image, where labeled training images and
unlabeled test images are from the same distribution and share the same label
set. However, in an open world, the unlabeled test images probably contain
unknown categories and have different distributions from the labeled images.
Hence, in this paper, we consider a new, more realistic, and more challenging
problem setting where the pixel-level classifier has to be trained with labeled
images and unlabeled open-world images -- we name it open-set domain adaptation
segmentation (OSDAS). In OSDAS, the trained classifier is expected to identify
unknown-class pixels and classify known-class pixels well. To solve OSDAS, we
first investigate which distribution that unknown-class pixels obey. Then,
motivated by the goodness-of-fit test, we use statistical measurements to show
how a pixel fits the distribution of an unknown class and select highly-fitted
pixels to form the unknown region in each test image. Eventually, we propose an
end-to-end learning framework, known-region-aware domain alignment (KRADA), to
distinguish unknown classes while aligning the distributions of known classes
in labeled and unlabeled open-world images. The effectiveness of KRADA has been
verified on two synthetic tasks and one COVID-19 segmentation task.
