  Domain adaptation (DA) paves the way for label annotation and dataset bias
issues by the knowledge transfer from a label-rich source domain to a related
but unlabeled target domain. A mainstream of DA methods is to align the feature
distributions of the two domains. However, the majority of them focus on the
entire image features where irrelevant semantic information, e.g., the messy
background, is inevitably embedded. Enforcing feature alignments in such case
will negatively influence the correct matching of objects and consequently lead
to the semantically negative transfer due to the confusion of irrelevant
semantics. To tackle this issue, we propose Semantic Concentration for Domain
Adaptation (SCDA), which encourages the model to concentrate on the most
principal features via the pair-wise adversarial alignment of prediction
distributions. Specifically, we train the classifier to class-wisely maximize
the prediction distribution divergence of each sample pair, which enables the
model to find the region with large differences among the same class of
samples. Meanwhile, the feature extractor attempts to minimize that
discrepancy, which suppresses the features of dissimilar regions among the same
class of samples and accentuates the features of principal parts. As a general
method, SCDA can be easily integrated into various DA methods as a regularizer
to further boost their performance. Extensive experiments on the cross-domain
benchmarks show the efficacy of SCDA.
