Depth Guidance and Intradomain Adaptation for Semantic Segmentation

Unsupervised domain adaptation (UDA) mitigates the domain shift between the source and target domains. Some conventional methods are based on guiding semantic segmentation across domains using depth information. Nevertheless, these methods are constrained and inadequate for the use of depth knowledge. In this work, we propose a depth-aware adaptation framework (DAF) and an intradomain adaptation (IDA) strategy to improve semantic segmentation in the context of UDA. We design a novel depth estimation network based on the channelwise attention mechanism to provide additional depth information. In our approach, DAF aims to adapt domains by exploiting the inherent correlations of semantic and depth information, where we construct a depth-aware space for the alignment between source and target domains. Furthermore, IDA bridges the gap within the target domain through a proposed depth-aware ranking strategy. This strategy is based on dividing the target domain into a subsource domain and a subtarget domain. The distribution discrepancies between these domains are then aligned. Extensive experiments on SYNTHIAb $\rightarrow $ Cityscapes and SYNTHIAb $\rightarrow $ Mapillary cross-domain tasks confirm that our method overperforms the conventional depth guidance methods with mean IOU values of 46.7% and 73.3% on the above two tasks, respectively.