Domain Generalized Object Detection with Triple Graph Reasoning Network

Zhijie Rao, Luyao Tang, Yue Huang, Xinghao Ding

Abstract: Recent advances in Domain Adaptive Object Detection (DAOD) have vastly restrained the performance degradation caused by distribution shift. However, DAOD relies on the strong assumption of accessible target domain during the learning procedure, which is tough to be satisfied in real-world applications. Domain Generalized Object Detection (DGOD) aims to generalize the detector trained on the source domains directing to an unknown target domain without accessing the target data. Thus it is a much more challenged problem and very few contributions have been reported. Extracting domain-invariant information is the key problem of domain generalization. Considering that the topological structure of objects does not change with the domain, we present a general DGOD framework, Triple Graph Reasoning Network (TGRN) to uncover and model the structure of objects. The proposed TGRN models the topological relations of foregrounds via building refined sparse graphs on both pixel-level and semantic-level. Meanwhile, a bipartite graph is created to capture structural consistency of instances across domain, implicitly enabling distribution alignment. Experiments on our newly constructed datasets verify the effectiveness of the proposed TGRN. Codes and datasets are available at https://github.com/zjrao/tgrn.