  Lacking enough high quality proposals for RoI box head has impeded two-stage
and multi-stage object detectors for a long time, and many previous works try
to solve it via improving RPN's performance or manually generating proposals
from ground truth. However, these methods either need huge training and
inference costs or bring little improvements. In this paper, we design a novel
training method named APDI, which means augmenting proposals by the detector
itself and can generate proposals with higher quality. Furthermore, APDI makes
it possible to integrate IoU head into RoI box head. And it does not add any
hyperparameter, which is beneficial for future research and downstream tasks.
Extensive experiments on COCO dataset show that our method brings at least 2.7
AP improvements on Faster R-CNN with various backbones, and APDI can cooperate
with advanced RPNs, such as GA-RPN and Cascade RPN, to obtain extra gains.
Furthermore, it brings significant improvements on Cascade R-CNN.
