  Conventional object detection models inevitably encounter a performance drop
as the domain disparity exists. Unsupervised domain adaptive object detection
is proposed recently to reduce the disparity between domains, where the source
domain is label-rich while the target domain is label-agnostic. The existing
models follow a parameter shared siamese structure for adversarial domain
alignment, which, however, easily leads to the collapse and out-of-control risk
of the source domain and brings negative impact to feature adaption. The main
reason is that the labeling unfairness (asymmetry) between source and target
makes the parameter sharing mechanism unable to adapt. Therefore, in order to
avoid the source domain collapse risk caused by parameter sharing, we propose
an asymmetric tri-way Faster-RCNN (ATF) for domain adaptive object detection.
Our ATF model has two distinct merits: 1) A ancillary net supervised by source
label is deployed to learn ancillary target features and simultaneously
preserve the discrimination of source domain, which enhances the structural
discrimination (object classification vs. bounding box regression) of domain
alignment. 2) The asymmetric structure consisting of a chief net and an
independent ancillary net essentially overcomes the parameter sharing aroused
source risk collapse. The adaption safety of the proposed ATF detector is
guaranteed. Extensive experiments on a number of datasets, including
Cityscapes, Foggy-cityscapes, KITTI, Sim10k, Pascal VOC, Clipart and
Watercolor, demonstrate the SOTA performance of our method.
