Mixup Regularization for Region Proposal based Object Detectors

Understand

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.