🤖 AI Summary
This work addresses the performance bottleneck of vision models caused by the scarcity of high-quality annotated data by proposing a modular synthetic data engine. The approach integrates controllable diffusion-based generation, multi-stage filtering, uncertainty-driven sample selection, and human-in-the-loop verification to establish a “synthetic-first” strategy calibrated by real data. Implemented via a command-line interface, the pipeline ensures reproducibility and flexible deployment. Evaluated on human pose estimation, the method achieves significantly better performance than real-data-only baselines when using only minimal real annotations augmented with synthetic data. Although purely synthetic training still lags behind, the results validate the effectiveness and practical utility of the proposed data curation framework in low-resource scenarios.
📝 Abstract
Modern computer vision systems increasingly encounter performance limitations in data-scarce domains, where collecting large-scale, high-quality labeled data is costly or impractical. While controllable diffusion models enable scalable synthetic image generation, directly applying synthetic augmentation often leads to unstable performance gains due to dataset-level quality issues and insufficient feedback mechanisms.
In this work, we present a Real-Calibrated Synthetic-First Data Engine, a modular data engineering framework that combines controllable diffusion generation and multi-stage curation/filtering within a unified pipeline, with optional support for uncertainty-driven selection and human verification. Instead of introducing new generative algorithms, our approach focuses on systematic dataset construction for improving the practical reliability of synthetic augmentation in low-data regimes.
The framework is implemented as a modular CLI-based pipeline, where generation, filtering, selection, and validation components can be independently configured and replaced. This design emphasizes reproducibility, flexibility, and practical deployment in real-world data workflows.
Through empirical evaluation centered on human pose estimation, we show that synthetic data improves a real-data baseline when used as near-zero-human-annotation-cost augmentation alongside real anchors, while synthetic-only training remains substantially below real-only performance. Supplementary segmentation diagnostics show the same domain-gap pattern. These results highlight the practical value of data-centric orchestration for low-data augmentation.