  Mixup - a neural network regularization technique based on linear
interpolation of labeled sample pairs - has stood out by its capacity to
improve model's robustness and generalizability through a surprisingly simple
formalism. However, its extension to the field of object detection remains
unclear as the interpolation of bounding boxes cannot be naively defined. In
this paper, we propose to leverage the inherent region mapping structure of
anchors to introduce a mixup-driven training regularization for region proposal
based object detectors. The proposed method is benchmarked on standard datasets
with challenging detection settings. Our experiments show an enhanced
robustness to image alterations along with an ability to decontextualize
detections, resulting in an improved generalization power.
