  In the paradigm of object detection, the decision head is an important part,
which affects detection performance significantly. Yet how to design a
high-performance decision head remains to be an open issue. In this paper, we
propose a novel approach to combine decision trees and deep neural networks in
an end-to-end learning manner for object detection. First, we disentangle the
decision choices and prediction values by plugging soft decision trees into
neural networks. To facilitate effective learning, we propose randomized
decision routing with node selective and associative losses, which can boost
the feature representative learning and network decision simultaneously.
Second, we develop the decision head for object detection with narrow branches
to generate the routing probabilities and masks, for the purpose of obtaining
divergent decisions from different nodes. We name this approach as the
randomized decision routing for object detection, abbreviated as R(Det)$^2$.
Experiments on MS-COCO dataset demonstrate that R(Det)$^2$ is effective to
improve the detection performance. Equipped with existing detectors, it
achieves $1.4\sim 3.6$\% AP improvement.
