Domain Adaptive Object Detection (DAOD) leverages a labeled domain to learn
an object detector generalizing to a novel domain free of annotations. Recent
advances align class-conditional distributions by narrowing down cross-domain
prototypes (class centers). Though great success,they ignore the significant
within-class variance and the domain-mismatched semantics within the training
batch, leading to a sub-optimal adaptation. To overcome these challenges, we
propose a novel SemantIc-complete Graph MAtching (SIGMA) framework for DAOD,
which completes mismatched semantics and reformulates the adaptation with graph
matching. Specifically, we design a Graph-embedded Semantic Completion module
(GSC) that completes mismatched semantics through generating hallucination
graph nodes in missing categories. Then, we establish cross-image graphs to
model class-conditional distributions and learn a graph-guided memory bank for
better semantic completion in turn. After representing the source and target
data as graphs, we reformulate the adaptation as a graph matching problem,
i.e., finding well-matched node pairs across graphs to reduce the domain gap,
which is solved with a novel Bipartite Graph Matching adaptor (BGM). In a
nutshell, we utilize graph nodes to establish semantic-aware node affinity and
leverage graph edges as quadratic constraints in a structure-aware matching
loss, achieving fine-grained adaptation with a node-to-node graph matching.
Extensive experiments verify that SIGMA outperforms existing works
significantly. Our code is available at
this https URL

该论文提出了一种新的 DAOD 框架 SIGMA，它通过匹配图模型解决了 DAOD 中存在的语义差异和类内差异问题，实验表明该框架的检测效果优于现有的方法。