  Instance segmentation requires a large number of training samples to achieve
satisfactory performance and benefits from proper data augmentation. To enlarge
the training set and increase the diversity, previous methods have investigated
using data annotation from other domain (e.g. bbox, point) in a weakly
supervised mechanism. In this paper, we present a simple, efficient and
effective method to augment the training set using the existing instance mask
annotations. Exploiting the pixel redundancy of the background, we are able to
improve the performance of Mask R-CNN for 1.7 mAP on COCO dataset and 3.3 mAP
on Pascal VOC dataset by simply introducing random jittering to objects.
Furthermore, we propose a location probability map based approach to explore
the feasible locations that objects can be placed based on local appearance
similarity. With the guidance of such map, we boost the performance of
R101-Mask R-CNN on instance segmentation from 35.7 mAP to 37.9 mAP without
modifying the backbone or network structure. Our method is simple to implement
and does not increase the computational complexity. It can be integrated into
the training pipeline of any instance segmentation model without affecting the
training and inference efficiency. Our code and models have been released at
https://github.com/GothicAi/InstaBoost
