CAFA: Cross-Modal Attentive Feature Alignment for Cross-Domain Urban Scene Segmentation

Autonomous driving systems rely heavily on semantic segmentation models for accurate and safe decision-making. High segmentation performance in real-world urban scenes is crucial for autonomous vehicles, while substantial pixel-level labels are required during model training. Unsupervised domain adaptation (UDA) techniques are widely used to adapt the segmentation model trained on the synthetic data (i.e., source domain) to the real-world data (i.e., target domain) since obtaining pixel-level annotations is fairly easy in the synthetic environment. Recently, increasing UDA approaches promote cross-domain semantic segmentation (CDSS) by fusing the depth information into the RGB features. However, feature fusion does not necessarily eliminate the domain-specific components in the RGB features, which can result in the features still being influenced by domain-specific information. To address this, we propose a novel cross-modal attentive feature alignment (CAFA) framework for CDSS, which provides an explicit perspective of using depth information to align the main backbone RGB features of both domains in a nonadversarial manner. In particular, considering that the depth modality is less affected by the domain gap, we employ depth as an intermediate modality and align the RGB features by attending RGB features to the depth modality through constructing an auxiliary multimodal segmentation task. The state-of-the-art performance of our CAFA can be achieved on benchmark tasks, such as Synthia $\to$ Cityscapes and grand theft auto (GTA) $\to$ Cityscapes.