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arXiv · 2212.04221

An Empirical Study on Multi-Domain Robust Semantic Segmentation

Yajie Liu·Pu Ge·Qingjie Liu·Shichao Fan·Yunhong Wang
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Abstract

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.

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Yajie Liu, Pu Ge, Qingjie Liu, Shichao Fan, Yunhong Wang. 2022-12-08. An Empirical Study on Multi-Domain Robust Semantic Segmentation. https://arxiv.org/abs/2212.04221

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