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.

本论文针对计算机视觉中重要的挑战之一 —— 构建数据有效且能处理罕见对象类别的实例分割模型，研究了一种使用数据增强方法的 Copy-Paste 方法，并发现简单的随机粘贴对象机制足以提供良好的性能，并且能够与半监督学习方法相结合获得更好的结果。在 COCO 实例分割任务和 LVIS 评测中，该方法均获得了优异的表现。