论文标题
简单的复制 - 帕斯特是一种强大的数据增强方法，例如分割
Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation
论文作者
论文摘要
在计算机视觉中，构建实例分割模型并可以处理稀有对象类别是重要的挑战。利用数据增强是应对这一挑战的有希望的方向。在这里，我们对拷贝性增强（[13，12]）进行系统研究，例如分割，我们将对象随机粘贴到图像上。关于复制纸的先前研究依赖于对周围的视觉上下文进行建模以粘贴对象。但是，我们发现随机粘贴物体的简单机制足够好，并且可以在强质基础上提供固体收益。此外，我们展示了拷贝性粘贴具有添加剂，并具有半监督的方法，这些方法通过伪标记（例如自我训练）利用额外的数据来利用额外的数据。在可可实例细分中，我们实现了49.1个掩码AP和57.3框AP，改进+0.6掩码AP和+1.5 Box AP比先前的最新时间。我们进一步证明，拷贝性可以导致LVIS基准的重大改进。我们的基线模型在稀有类别上优于LVIS 2020挑战赛挑战率+3.6 Mask AP。
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.
