  Cross-Domain Detection (XDD) aims to train an object detector using labeled
image from a source domain but have good performance in the target domain with
only unlabeled images. Existing approaches achieve this either by aligning the
feature maps or the region proposals from the two domains, or by transferring
the style of source images to that of target image. Contrasted with prior work,
this paper provides a complementary solution to align domains by learning the
same auxiliary tasks in both domains simultaneously. These auxiliary tasks push
image from both domains towards shared spaces, which bridges the domain gap.
Specifically, this paper proposes Rotation Prediction and Consistency Learning
(PRCL), a framework complementing existing XDD methods for domain alignment by
leveraging the two auxiliary tasks. The first one encourages the model to
extract region proposals from foreground regions by rotating an image and
predicting the rotation angle from the extracted region proposals. The second
task encourages the model to be robust to changes in the image space by
optimizing the model to make consistent class predictions for region proposals
regardless of image perturbations. Experiments show the detection performance
can be consistently and significantly enhanced by applying the two proposed
tasks to existing XDD methods.
