  Object recognition from images means to automatically find object(s) of
interest and to return their category and location information. Benefiting from
research on deep learning, like convolutional neural networks~(CNNs) and
generative adversarial networks, the performance in this field has been
improved significantly, especially when training and test data are drawn from
similar distributions. However, mismatching distributions, i.e., domain shifts,
lead to a significant performance drop. In this paper, we build
domain-invariant detectors by learning domain classifiers via adversarial
training. Based on the previous works that align image and instance level
features, we mitigate the domain shift further by introducing a domain
adaptation component at the region level within Faster \mbox{R-CNN}. We embed a
domain classification network in the region proposal network~(RPN) using
adversarial learning. The RPN can now generate accurate region proposals in
different domains by effectively aligning the features between them. To
mitigate the unstable convergence during the adversarial learning, we introduce
a balanced domain classifier as well as a network learning rate adjustment
strategy. We conduct comprehensive experiments using four standard datasets.
The results demonstrate the effectiveness and robustness of our object
detection approach in domain shift scenarios.
