This paper describes a method of domain adaptive training for semantic segmentation using multiple source datasets that are not necessarily relevant to the target dataset. We propose a soft pseudo-label generation method by integrating predicted object probabilities from multiple source models. The prediction of each source model is weighted based on the estimated domain similarity between the source and the target datasets to emphasize contribution of a model trained on a source that is more similar to the target and generate reasonable pseudo-labels. We also propose a training method using the soft pseudo-labels considering their entropy to fully exploit information from the source datasets while suppressing the influence of possibly misclassified pixels. The experiments show comparative or better performance than our previous work and another existing multi-source domain adaptation method, and applicability to a variety of target environments.

翻译：本文描述了一种使用多个与目标数据集不一定相关的源数据集进行语义分割的域自适应训练方法。我们提出了一种软伪标签生成方法，通过整合来自多个源模型的预测对象概率来实现。每个源模型的预测基于源域与目标域之间估计的域相似性进行加权，以强调在更接近目标的源上训练的模型的贡献，从而生成合理的伪标签。我们还提出了一种使用考虑其熵的软伪标签的训练方法，以便充分利用源数据集的信息，同时抑制可能错误分类像素的影响。实验表明，该方法与之前的工作及现有的另一种多源域适应方法相比表现相当或更优，且适用于各种目标环境。