@inproceedings{
qian2024maskfactory,
title={MaskFactory: Towards High-quality Synthetic Data Generation for Dichotomous Image Segmentation},
author={Haotian Qian and Yinda Chen and Shengtao Lou and Fahad Khan and Xiaogang Jin and Deng-Ping Fan},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=iM5i289eqt}
}
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, Mask Factory, 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 https://qian-hao-tian.github.io/MaskFactory/.
MaskFactory: Towards High-quality Synthetic Data Generation for Dichotomous Image Segmentation
Haotian Qian Yinda Chen Shengtao Lou Fahad Khan Xiaogang Jin Deng-Ping Fan