Dataset Augmentation with Synthetic Images Improves Semantic Segmentation

TL;DR

This paper demonstrates that augmenting weakly annotated datasets with synthetic images significantly improves semantic segmentation performance, reducing annotation effort and dataset collection costs.

Contribution

The paper introduces a method of using synthetic images for dataset augmentation, enhancing semantic segmentation accuracy with minimal additional annotation effort.

Findings

Mean IOU increased from 52.80% to 55.47% with synthetic images.

Adding 100 synthetic images per class yields notable performance gains.

Synthetic augmentation reduces annotation and data collection costs.

Abstract

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.