  Real-world object detectors are often challenged by the domain gaps between
different datasets. In this work, we present the Conditional Domain
Normalization (CDN) to bridge the domain gap. CDN is designed to encode
different domain inputs into a shared latent space, where the features from
different domains carry the same domain attribute. To achieve this, we first
disentangle the domain-specific attribute out of the semantic features from one
domain via a domain embedding module, which learns a domain-vector to
characterize the corresponding domain attribute information. Then this
domain-vector is used to encode the features from another domain through a
conditional normalization, resulting in different domains' features carrying
the same domain attribute. We incorporate CDN into various convolution stages
of an object detector to adaptively address the domain shifts of different
level's representation. In contrast to existing adaptation works that conduct
domain confusion learning on semantic features to remove domain-specific
factors, CDN aligns different domain distributions by modulating the semantic
features of one domain conditioned on the learned domain-vector of another
domain. Extensive experiments show that CDN outperforms existing methods
remarkably on both real-to-real and synthetic-to-real adaptation benchmarks,
including 2D image detection and 3D point cloud detection.
