🤖 AI Summary
This work addresses the distribution shift between pre-trained models and real-world deployment environments by proposing a three-stage automated pipeline that leverages diffusion models to generate high-quality, domain-specific synthetic datasets. The approach begins with controlled image inpainting to synthesize objects within target-domain backgrounds, followed by multimodal quality assessment—encompassing object detection accuracy, aesthetic scoring, and vision-language alignment—to evaluate the fidelity of generated samples. Finally, a user preference classifier is introduced to filter outputs according to subjective human standards. To the best of our knowledge, this is the first method to integrate controlled image inpainting, multimodal evaluation, and user preference modeling into an end-to-end framework, substantially reducing reliance on large-scale real-world data collection while efficiently producing deployment-ready domain datasets.
📝 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.