  We present a novel approach to perform the unsupervised domain adaptation for
object detection through forward-backward cyclic (FBC) training. Recent
adversarial training based domain adaptation methods have shown their
effectiveness on minimizing domain discrepancy via marginal feature
distributions alignment. However, aligning the marginal feature distributions
does not guarantee the alignment of class conditional distributions. This
limitation is more evident when adapting object detectors as the domain
discrepancy is larger compared to the image classification task, e.g. various
number of objects exist in one image and the majority of content in an image is
the background. This motivates us to learn domain invariance for category level
semantics via gradient alignment. Intuitively, if the gradients of two domains
point in similar directions, then the learning of one domain can improve that
of another domain. To achieve gradient alignment, we propose Forward-Backward
Cyclic Adaptation, which iteratively computes adaptation from source to target
via backward hopping and from target to source via forward passing. In
addition, we align low-level features for adapting holistic color/texture via
adversarial training. However, the detector performs well on both domains is
not ideal for target domain. As such, in each cycle, domain diversity is
enforced by maximum entropy regularization on the source domain to penalize
confident source-specific learning and minimum entropy regularization on target
domain to intrigue target-specific learning. Theoretical analysis of the
training process is provided, and extensive experiments on challenging
cross-domain object detection datasets have shown the superiority of our
approach over the state-of-the-art.
