擦除，然后重绘：一种使用扩散模型进行自由空间检测的新型数据增强方法
Erase, then Redraw: A Novel Data Augmentation Approach for Free Space Detection Using Diffusion Model
AI总结：
针对道路检测任务中传统数据增强缺乏语义多样性的问题，提出一种利用预训练扩散模型擦除原图实例并重绘新实例的数据增强方法，在 KITTI 数据集上取得最优结果。
摘要：
数据增强是深度学习中最常用的工具之一，支撑了许多近期的进展，包括分类、检测和语义分割等任务。标准的数据增强方法涉及旋转和翻转等简单变换以生成新图像。然而，这些新图像往往缺乏数据内主要语义维度上的多样性。传统数据增强方法无法改变场景中车辆、树木和建筑物等高级语义属性的存在，从而无法增强数据多样性。近年来，生成模型的快速发展为数据增强领域注入了新的活力。在本文中，我们通过使用预训练的文本到图像扩散模型来参数化图像到图像的转换，解决了道路检测任务中数据增强缺乏多样性的问题。我们的方法涉及使用这些扩散模型编辑图像以改变其语义。本质上，我们通过从原始数据集中擦除真实对象的实例，并使用扩散模型在擦除区域生成具有相似语义的新实例来实现这一目标，从而扩展了原始数据集。我们在 KITTI 道路数据集上评估了我们的方法，并与其他数据增强方法相比取得了最佳结果，这证明了我们提出的开发的有效性。
英文摘要：
Data augmentation is one of the most common tools in deep learning, underpinning many recent advances including tasks such as classification, detection, and semantic segmentation. The standard approach to data augmentation involves simple transformations like rotation and flipping to generate new images. However, these new images often lack diversity along the main semantic dimensions within the data. Traditional data augmentation methods cannot alter high-level semantic attributes such as the presence of vehicles, trees, and buildings in a scene to enhance data diversity. In recent years, the rapid development of generative models has injected new vitality into the field of data augmentation. In this paper, we address the lack of diversity in data augmentation for road detection task by using a pre-trained text-to-image diffusion model to parameterize image-to-image transformations. Our method involves editing images using these diffusion models to change their semantics. In essence, we achieve this goal by erasing instances of real objects from the original dataset and generating new instances with similar semantics in the erased regions using the diffusion model, thereby expanding the original dataset. We evaluate our approach on the KITTI road dataset and achieve the best results compared to other data augmentation methods, which demonstrates the effectiveness of our proposed development.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
