Many deep learning tasks require annotations that are too time consuming for human operators, resulting in small dataset sizes. This is especially true for dense regression problems such as crowd counting which requires the location of every person in the image to be annotated. Techniques such as data augmentation and synthetic data generation based on simulations can help in such cases. In this paper, we introduce PromptMix, a method for artificially boosting the size of existing datasets, that can be used to improve the performance of lightweight networks. First, synthetic images are generated in an end-to-end data-driven manner, where text prompts are extracted from existing datasets via an image captioning deep network, and subsequently introduced to text-to-image diffusion models. The generated images are then annotated using one or more high-performing deep networks, and mixed with the real dataset for training the lightweight network. By extensive experiments on five datasets and two tasks, we show that PromptMix can significantly increase the performance of lightweight networks by up to 26%.

翻译：许多深度学习任务需要人工耗时标注，导致数据集规模较小，尤其在密集回归问题（如人群计数）中，需对图像中每个人的位置进行标注。数据增强和基于仿真的合成数据生成技术可缓解此类问题。本文提出PromptMix方法，通过人工扩展现有数据集规模，提升轻量级网络性能。首先，以数据驱动端到端方式生成合成图像：利用图像描述深度网络从现有数据集中提取文本提示，并将其输入文本到图像扩散模型；随后，使用一个或多个高性能深度网络对生成图像进行标注，并与真实数据集混合，用于训练轻量级网络。通过在五个数据集和两个任务上的大量实验，我们证明PromptMix可显著提升轻量级网络性能，最高提升达26%。