  Although Deep Convolutional Neural Networks trained with strong pixel-level
annotations have significantly pushed the performance in semantic segmentation,
annotation efforts required for the creation of training data remains a
roadblock for further improvements. We show that augmentation of the weakly
annotated training dataset with synthetic images minimizes both the annotation
efforts and also the cost of capturing images with sufficient variety.
Evaluation on the PASCAL 2012 validation dataset shows an increase in mean IOU
from 52.80% to 55.47% by adding just 100 synthetic images per object class. Our
approach is thus a promising solution to the problems of annotation and dataset
collection.
