  In real applications, object detectors based on deep networks still face
challenges of the large domain gap between the labeled training data and
unlabeled testing data. To reduce the gap, recent techniques are proposed by
aligning the image/instance-level features between source and unlabeled target
domains. However, these methods suffer from the suboptimal problem mainly
because of ignoring the category information of object instances. To tackle
this issue, we develop a fine-grained domain alignment approach with a
well-designed domain classifier bank that achieves the instance-level alignment
respecting to their categories. Specifically, we first employ the mean teacher
paradigm to generate pseudo labels for unlabeled samples. Then we implement the
class-level domain classifiers and group them together, called domain
classifier bank, in which each domain classifier is responsible for aligning
features of a specific class. We assemble the bare object detector with the
proposed fine-grained domain alignment mechanism as the adaptive detector, and
optimize it with a developed crossed adaptive weighting mechanism. Extensive
experiments on three popular transferring benchmarks demonstrate the
effectiveness of our method and achieve the new remarkable state-of-the-arts.
