  Generic object detection has been immensely promoted by the development of
deep convolutional neural networks in the past decade. However, in the domain
shift circumstance, the changes in weather, illumination, etc., often cause
domain gap, and thus performance drops substantially when detecting objects
from one domain to another. Existing methods on this task usually draw
attention on the high-level alignment based on the whole image or object of
interest, which naturally, cannot fully utilize the fine-grained channel
information. In this paper, we realize adaptation from a thoroughly different
perspective, i.e., channel-wise alignment. Motivated by the finding that each
channel focuses on a specific pattern (e.g., on special semantic regions, such
as car), we aim to align the distribution of source and target domain on the
channel level, which is finer for integration between discrepant domains. Our
method mainly consists of self channel-wise and cross channel-wise alignment.
These two parts explore the inner-relation and cross-relation of attention
regions implicitly from the view of channels. Further more, we also propose a
RPN domain classifier module to obtain a domain-invariant RPN network.
Extensive experiments show that the proposed method performs notably better
than existing methods with about 5% improvement under various domain-shift
settings. Experiments on different task (e.g. instance segmentation) also
demonstrate its good scalability.
