用扩散模型自动生成适配真实场景的合成数据集
From Prompts to Deployment: Auto-Curated Domain-Specific Dataset Generation via Diffusion Models
- 通过可控修复生成特定背景下的目标物体
- 多模态评估确保生成数据兼具准确与美观
- 融合用户偏好筛选，适合实际部署需求
本文提出一种基于扩散模型的自动化流程，用于生成领域特定的合成数据集，以缓解预训练模型与真实部署环境间的分布偏移问题。该三阶段框架首先在特定领域背景中通过可控修复合成目标物体；生成结果经多模态评估，包括目标检测性能、美学评分及视觉-语言对齐度；最后利用用户偏好分类器捕捉主观选择标准。该流程可高效构建高质量、可部署的数据集，显著减少对大量真实数据采集的依赖。
原文摘要 · Abstract (English)
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.
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