  Applying the knowledge of an object detector trained on a specific domain
directly onto a new domain is risky, as the gap between two domains can
severely degrade model's performance. Furthermore, since different instances
commonly embody distinct modal information in object detection scenario, the
feature alignment of source and target domain is hard to be realized. To
mitigate these problems, we propose a Graph-induced Prototype Alignment (GPA)
framework to seek for category-level domain alignment via elaborate prototype
representations. In the nutshell, more precise instance-level features are
obtained through graph-based information propagation among region proposals,
and, on such basis, the prototype representation of each class is derived for
category-level domain alignment. In addition, in order to alleviate the
negative effect of class-imbalance on domain adaptation, we design a
Class-reweighted Contrastive Loss to harmonize the adaptation training process.
Combining with Faster R-CNN, the proposed framework conducts feature alignment
in a two-stage manner. Comprehensive results on various cross-domain detection
tasks demonstrate that our approach outperforms existing methods with a
remarkable margin. Our code is available at
https://github.com/ChrisAllenMing/GPA-detection.
