  Domain adaptive semantic segmentation attempts to make satisfactory dense
predictions on an unlabeled target domain by utilizing the supervised model
trained on a labeled source domain. In this work, we propose Semantic-Guided
Pixel Contrast (SePiCo), a novel one-stage adaptation framework that highlights
the semantic concepts of individual pixels to promote learning of
class-discriminative and class-balanced pixel representations across domains,
eventually boosting the performance of self-training methods. Specifically, to
explore proper semantic concepts, we first investigate a centroid-aware pixel
contrast that employs the category centroids of the entire source domain or a
single source image to guide the learning of discriminative features.
Considering the possible lack of category diversity in semantic concepts, we
then blaze a trail of distributional perspective to involve a sufficient
quantity of instances, namely distribution-aware pixel contrast, in which we
approximate the true distribution of each semantic category from the statistics
of labeled source data. Moreover, such an optimization objective can derive a
closed-form upper bound by implicitly involving an infinite number of
(dis)similar pairs, making it computationally efficient. Extensive experiments
show that SePiCo not only helps stabilize training but also yields
discriminative representations, making significant progress on both
synthetic-to-real and daytime-to-nighttime adaptation scenarios.
