  Unsupervised domain adaptation is critical in various computer vision tasks,
such as object detection, instance segmentation, etc. They attempt to reduce
domain bias-induced performance degradation while also promoting model
application speed. Previous works in domain adaptation object detection attempt
to align image-level and instance-level shifts to eventually minimize the
domain discrepancy, but they may align single-class features to mixed-class
features in image-level domain adaptation because each image in the object
detection task may be more than one class and object. In order to achieve
single-class with single-class alignment and mixed-class with mixed-class
alignment, we treat the mixed-class of the feature as a new class and propose a
mixed-classes $H-divergence$ for object detection to achieve homogenous feature
alignment and reduce negative transfer. Then, a Semantic Consistency Feature
Alignment Model (SCFAM) based on mixed-classes $H-divergence$ was also
presented. To improve single-class and mixed-class semantic information and
accomplish semantic separation, the SCFAM model proposes Semantic Prediction
Models (SPM) and Semantic Bridging Components (SBC). And the weight of the pix
domain discriminator loss is then changed based on the SPM result to reduce
sample imbalance. Extensive unsupervised domain adaption experiments on widely
used datasets illustrate our proposed approach's robust object detection in
domain bias settings.
