How to effectively leverage the plentiful existing datasets to train a robust and high-performance model is of great significance for many practical applications. However, a model trained on a naive merge of different datasets tends to obtain poor performance due to annotation conflicts and domain divergence.In this paper, we attempt to train a unified model that is expected to perform well across domains on several popularity segmentation datasets.We conduct a detailed analysis of the impact on model generalization from three aspects of data augmentation, training strategies, and model capacity.Based on the analysis, we propose a robust solution that is able to improve model generalization across domains.Our solution ranks 2nd on RVC 2022 semantic segmentation task, with a dataset only 1/3 size of the 1st model used.
@article{arxiv.2212.04221,
title = {An Empirical Study on Multi-Domain Robust Semantic Segmentation},
author = {Yajie Liu and Pu Ge and Qingjie Liu and Shichao Fan and Yunhong Wang},
journal= {arXiv preprint arXiv:2212.04221},
year = {2022}
}