Prototypical contrastive learning (PCL) has been widely used to learn class-wise domain-invariant features recently. These methods are based on the assumption that the prototypes, which are represented as the central value of the same class in a certain domain, are domain-invariant. Since the prototypes of different domains have discrepancies as well, the class-wise domain-invariant features learned from the source domain by PCL need to be aligned with the prototypes of other domains simultaneously. However, the prototypes of the same class in different domains may be different while the prototypes of different classes may be similar, which may affect the learning of class-wise domain-invariant features. Based on these observations, a calibration-based dual prototypical contrastive learning (CDPCL) approach is proposed to reduce the domain discrepancy between the learned class-wise features and the prototypes of different domains for domain generalization semantic segmentation. It contains an uncertainty-guided PCL (UPCL) and a hard-weighted PCL (HPCL). Since the domain discrepancies of the prototypes of different classes may be different, we propose an uncertainty probability matrix to represent the domain discrepancies of the prototypes of all the classes. The UPCL estimates the uncertainty probability matrix to calibrate the weights of the prototypes during the PCL. Moreover, considering that the prototypes of different classes may be similar in some circumstances, which means these prototypes are hard-aligned, the HPCL is proposed to generate a hard-weighted matrix to calibrate the weights of the hard-aligned prototypes during the PCL. Extensive experiments demonstrate that our approach achieves superior performance over current approaches on domain generalization semantic segmentation tasks.

翻译：原型对比学习（PCL）近年来被广泛用于学习类别级域不变特征。这些方法基于一个假设：表示为同一域内同类中心值的原型具有域不变性。由于不同域的原型也存在差异，通过PCL从源域学习到的类别级域不变特征需同时与其他域的原型对齐。然而，同一类别在不同域中的原型可能不同，而不同类别的原型可能相似，这会影响类别级域不变特征的学习。基于这些观察，提出了一种基于校准的双原型对比学习（CDPCL）方法，以减少学习到的类别级特征与不同域原型之间的域差异，用于域泛化语义分割。该方法包括不确定性引导的PCL（UPCL）和硬加权PCL（HPCL）。考虑到不同类别原型的域差异可能不同，我们提出了一种不确定性概率矩阵来表示所有类别原型的域差异。UPCL通过估计不确定性概率矩阵来在校准PCL过程中调整原型的权重。此外，考虑到不同类别的原型在某些情况下可能相似（即这些原型的对齐较困难），提出了HPCL生成硬加权矩阵，以在校准PCL过程中调整这些难对齐原型的权重。大量实验证明，我们的方法在域泛化语义分割任务中优于现有方法。