  The recently proposed panoptic segmentation task presents a significant
challenge of image understanding with computer vision by unifying semantic
segmentation and instance segmentation tasks. In this paper we present an
efficient and novel panoptic data augmentation (PanDA) method which operates
exclusively in pixel space, requires no additional data or training, and is
computationally cheap to implement. By retraining original state-of-the-art
models on PanDA augmented datasets generated with a single frozen set of
parameters, we show robust performance gains in panoptic segmentation, instance
segmentation, as well as detection across models, backbones, dataset domains,
and scales. Finally, the effectiveness of unrealistic-looking training images
synthesized by PanDA suggest that one should rethink the need for image realism
for efficient data augmentation.
