  Recent advances in unsupervised domain adaptation (UDA) techniques have
witnessed great success in cross-domain computer vision tasks, enhancing the
generalization ability of data-driven deep learning architectures by bridging
the domain distribution gaps. For the UDA-based cross-domain object detection
methods, the majority of them alleviate the domain bias by inducing the
domain-invariant feature generation via adversarial learning strategy. However,
their domain discriminators have limited classification ability due to the
unstable adversarial training process. Therefore, the extracted features
induced by them cannot be perfectly domain-invariant and still contain
domain-private factors, bringing obstacles to further alleviate the
cross-domain discrepancy. To tackle this issue, we design a Domain
Disentanglement Faster-RCNN (DDF) to eliminate the source-specific information
in the features for detection task learning. Our DDF method facilitates the
feature disentanglement at the global and local stages, with a Global Triplet
Disentanglement (GTD) module and an Instance Similarity Disentanglement (ISD)
module, respectively. By outperforming state-of-the-art methods on four
benchmark UDA object detection tasks, our DDF method is demonstrated to be
effective with wide applicability.
