Deep neural networks are typically trained in a single shot for a specific
task and data distribution, but in real world settings both the task and the
domain of application can change. The problem becomes even more challenging in
dense predictive tasks, such as semantic segmentation, and furthermore most
approaches tackle the two problems separately. In this paper we introduce the
novel task of coarse-to-fine learning of semantic segmentation architectures in
presence of domain shift. We consider subsequent learning stages progressively
refining the task at the semantic level; i.e., the finer set of semantic labels
at each learning step is hierarchically derived from the coarser set of the
previous step. We propose a new approach (CCDA) to tackle this scenario. First,
we employ the maximum squares loss to align source and target domains and, at
the same time, to balance the gradients between well-classified and harder
samples. Second, we introduce a novel coarse-to-fine knowledge distillation
constraint to transfer network capabilities acquired on a coarser set of labels
to a set of finer labels. Finally, we design a coarse-to-fine weight
initialization rule to spread the importance from each coarse class to the
respective finer classes. To evaluate our approach, we design two benchmarks
where source knowledge is extracted from the GTA5 dataset and it is transferred
to either the Cityscapes or the IDD datasets, and we show how it outperforms
the main competitors.

本文提出了一种新的粗到细的语义分割架构的学习任务，以应对不同数据集和任务的领域变化，并设计了一种新的方法，使用最大平方损失来对齐源和目标领域，引入了知识蒸馏约束来传递网络能力，以及设计了粗细权重初始化规则。在实验中表明该方法优于主要竞争对手。