In computer vision, it is well-known that a lack of data diversity will impair model performance. In this study, we address the challenges of enhancing the dataset diversity problem in order to benefit various downstream tasks such as object detection and instance segmentation. We propose a simple yet effective data augmentation approach by leveraging advancements in generative models, specifically text-to-image synthesis technologies like Stable Diffusion. Our method focuses on generating variations of labeled real images, utilizing generative object and background augmentation via inpainting to augment existing training data without the need for additional annotations. We find that background augmentation, in particular, significantly improves the models' robustness and generalization capabilities. We also investigate how to adjust the prompt and mask to ensure the generated content comply with the existing annotations. The efficacy of our augmentation techniques is validated through comprehensive evaluations of the COCO dataset and several other key object detection benchmarks, demonstrating notable enhancements in model performance across diverse scenarios. This approach offers a promising solution to the challenges of dataset enhancement, contributing to the development of more accurate and robust computer vision models.

本研究解决了数据集多样性不足带来的问题，提出了一种简单有效的数据增强方法，通过生成模型，特别是文本到图像合成技术，生成带标签的真实图像变体。背景增强显著提高了模型的鲁棒性和泛化能力，经过在COCO数据集及其他物体检测基准的评估，证实了该方法在不同场景中的显著性能提升。

一种简单的背景增强方法用于扩散模型的物体检测