  To improve the generalization of detectors, for domain adaptive object
detection (DAOD), recent advances mainly explore aligning feature-level
distributions between the source and single-target domain, which may neglect
the impact of domain-specific information existing in the aligned features.
Towards DAOD, it is important to extract domain-invariant object
representations. To this end, in this paper, we try to disentangle
domain-invariant representations from domain-specific representations. And we
propose a novel disentangled method based on vector decomposition. Firstly, an
extractor is devised to separate domain-invariant representations from the
input, which are used for extracting object proposals. Secondly,
domain-specific representations are introduced as the differences between the
input and domain-invariant representations. Through the difference operation,
the gap between the domain-specific and domain-invariant representations is
enlarged, which promotes domain-invariant representations to contain more
domain-irrelevant information. In the experiment, we separately evaluate our
method on the single- and compound-target case. For the single-target case,
experimental results of four domain-shift scenes show our method obtains a
significant performance gain over baseline methods. Moreover, for the
compound-target case (i.e., the target is a compound of two different domains
without domain labels), our method outperforms baseline methods by around 4%,
which demonstrates the effectiveness of our method.
