Domain Adaptation Frameworks for Road Scene Segmentation in Unseen Environments

本研究的目的是为基于深度学习的道路场景图像语义分割开发领域泛化框架。我们调查了多种最先进的现有方法，并确定了下划线超参数，这些参数导致了源域上的模型过度拟合，从而导致泛化能力较差。我们还确定了各种对域泛化非常有用的技术，比如在特征空间中混合样式，以及对比度、亮度、饱和度和色调等颜色抖动参数的良好范围。我们观察到，我们的发现在我们测试的所有基准目标域中都是一致的。基于这些发现，我们开发了一个用于领域泛化的集成模型，并取得了很好的性能。

The objective of this research is to develop domain generalization frameworks for the task of semantic segmentation of road scene images using deep learning. We surveyed multiple state-of-the-art existing methods and identified underline hyper-parameters that led to the overfitting of models on the source domains which led to poor generalization. We also identified various techniques that are very useful for domain generalization like mixing styles in feature space and the good range for color jittering paramters like contrast, brightness, saturation, and hue. We observed that our findings are consistent across all the benchmark target domains that we tested. Based on these findings, we have developed an ensemble model for domain generalization which achieves very good performance.