  Domain Adaptive Object Detection (DAOD) focuses on improving the
generalization ability of object detectors via knowledge transfer. Recent
advances in DAOD strive to change the emphasis of the adaptation process from
global to local in virtue of fine-grained feature alignment methods. However,
both the global and local alignment approaches fail to capture the topological
relations among different foreground objects as the explicit dependencies and
interactions between and within domains are neglected. In this case, only
seeking one-vs-one alignment does not necessarily ensure the precise knowledge
transfer. Moreover, conventional alignment-based approaches may be vulnerable
to catastrophic overfitting regarding those less transferable regions (e.g.
backgrounds) due to the accumulation of inaccurate localization results in the
target domain. To remedy these issues, we first formulate DAOD as an open-set
domain adaptation problem, in which the foregrounds and backgrounds are seen as
the ``known classes'' and ``unknown class'' respectively. Accordingly, we
propose a new and general framework for DAOD, named Foreground-aware
Graph-based Relational Reasoning (FGRR), which incorporates graph structures
into the detection pipeline to explicitly model the intra- and inter-domain
foreground object relations on both pixel and semantic spaces, thereby endowing
the DAOD model with the capability of relational reasoning beyond the popular
alignment-based paradigm. The inter-domain visual and semantic correlations are
hierarchically modeled via bipartite graph structures, and the intra-domain
relations are encoded via graph attention mechanisms. Empirical results
demonstrate that the proposed FGRR exceeds the state-of-the-art performance on
four DAOD benchmarks.
