Abstract
In this paper, we present an automated pipeline for generating domain-specific synthetic datasets with diffusion models, addressing the distribution shift between pre-trained models and real-world deployment environments. Our three-stage framework first synthesizes target objects within domain-specific backgrounds through controlled inpainting. The generated outputs are then validated via a multi-modal assessment that integrates object detection, aesthetic scoring, and vision-language alignment. Finally, a user-preference classifier is employed to capture subjective selection criteria. This pipeline enables the efficient construction of high-quality, deployable datasets while reducing reliance on extensive real-world data collection.
Abstract (translated)
在这篇论文中，我们提出了一种使用扩散模型生成特定领域合成数据集的自动化流水线，以解决预训练模型与现实世界部署环境之间的分布偏移问题。我们的三阶段框架首先通过受控修复（inpainting）在特定领域的背景下合成了目标对象。接下来，通过对物体检测、美学评分和视觉-语言对齐进行多模态评估来验证生成的输出。最后，使用用户偏好分类器捕捉主观选择标准。该流水线能够高效地构建高质量且可部署的数据集，并减少了对大量现实世界数据收集的需求。
URL
https://arxiv.org/abs/2601.08095
