Mask Factory: Towards High-quality Synthetic Data Generation for Dichotomous Image Segmentation
This addresses the labor-intensive and costly dataset creation for DIS tasks, offering a scalable solution, though it appears incremental as it builds on existing generative techniques.
The paper tackles the problem of generating high-quality synthetic data for Dichotomous Image Segmentation (DIS) by introducing Mask Factory, which reduces preparation time and costs while producing diverse and precise datasets, as demonstrated by superior performance on the DIS5K benchmark.
Dichotomous Image Segmentation (DIS) tasks require highly precise annotations, and traditional dataset creation methods are labor intensive, costly, and require extensive domain expertise. Although using synthetic data for DIS is a promising solution to these challenges, current generative models and techniques struggle with the issues of scene deviations, noise-induced errors, and limited training sample variability. To address these issues, we introduce a novel approach, \textbf{\ourmodel{}}, which provides a scalable solution for generating diverse and precise datasets, markedly reducing preparation time and costs. We first introduce a general mask editing method that combines rigid and non-rigid editing techniques to generate high-quality synthetic masks. Specially, rigid editing leverages geometric priors from diffusion models to achieve precise viewpoint transformations under zero-shot conditions, while non-rigid editing employs adversarial training and self-attention mechanisms for complex, topologically consistent modifications. Then, we generate pairs of high-resolution image and accurate segmentation mask using a multi-conditional control generation method. Finally, our experiments on the widely-used DIS5K dataset benchmark demonstrate superior performance in quality and efficiency compared to existing methods. The code is available at \url{https://qian-hao-tian.github.io/MaskFactory/}.