  Building instance segmentation models that are data-efficient and can handle
rare object categories is an important challenge in computer vision. Leveraging
data augmentations is a promising direction towards addressing this challenge.
Here, we perform a systematic study of the Copy-Paste augmentation ([13, 12])
for instance segmentation where we randomly paste objects onto an image. Prior
studies on Copy-Paste relied on modeling the surrounding visual context for
pasting the objects. However, we find that the simple mechanism of pasting
objects randomly is good enough and can provide solid gains on top of strong
baselines. Furthermore, we show Copy-Paste is additive with semi-supervised
methods that leverage extra data through pseudo labeling (e.g. self-training).
On COCO instance segmentation, we achieve 49.1 mask AP and 57.3 box AP, an
improvement of +0.6 mask AP and +1.5 box AP over the previous state-of-the-art.
We further demonstrate that Copy-Paste can lead to significant improvements on
the LVIS benchmark. Our baseline model outperforms the LVIS 2020 Challenge
winning entry by +3.6 mask AP on rare categories.
