  Unpaired image-to-image (I2I) translation often requires to maximize the
mutual information between the source and the translated images across
different domains, which is critical for the generator to keep the source
content and prevent it from unnecessary modifications. The self-supervised
contrastive learning has already been successfully applied in the I2I. By
constraining features from the same location to be closer than those from
different ones, it implicitly ensures the result to take content from the
source. However, previous work uses the features from random locations to
impose the constraint, which may not be appropriate since some locations
contain less information of source domain. Moreover, the feature itself does
not reflect the relation with others. This paper deals with these problems by
intentionally selecting significant anchor points for contrastive learning. We
design a query-selected attention (QS-Attn) module, which compares feature
distances in the source domain, giving an attention matrix with a probability
distribution in each row. Then we select queries according to their measurement
of significance, computed from the distribution. The selected ones are regarded
as anchors for contrastive loss. At the same time, the reduced attention matrix
is employed to route features in both domains, so that source relations
maintain in the synthesis. We validate our proposed method in three different
I2I datasets, showing that it increases the image quality without adding
learnable parameters.
