  We propose a novel method that tackles the problem of unsupervised domain
adaptation for semantic segmentation by maximizing the cosine similarity
between the source and the target domain at the feature level. A segmentation
network mainly consists of two parts, a feature extractor and a classification
head. We expect that if we can make the two domains have small domain gap at
the feature level, they would also have small domain discrepancy at the
classification head. Our method computes a cosine similarity matrix between the
source feature map and the target feature map, then we maximize the elements
exceeding a threshold to guide the target features to have high similarity with
the most similar source feature. Moreover, we use a class-wise source feature
dictionary which stores the latest features of the source domain to prevent the
unmatching problem when computing the cosine similarity matrix and be able to
compare a target feature with various source features from various images.
Through extensive experiments, we verify that our method gains performance on
two unsupervised domain adaptation tasks (GTA5$\to$ Cityscaspes and
SYNTHIA$\to$ Cityscapes).
