  Semantic segmentation is an important task for intelligent vehicles to
understand the environment. Current deep learning methods require large amounts
of labeled data for training. Manual annotation is expensive, while simulators
can provide accurate annotations. However, the performance of the semantic
segmentation model trained with the data of the simulator will significantly
decrease when applied in the actual scene. Unsupervised domain adaptation (UDA)
for semantic segmentation has recently gained increasing research attention,
aiming to reduce the domain gap and improve the performance on the target
domain. In this paper, we propose a novel two-stage entropy-based UDA method
for semantic segmentation. In stage one, we design a threshold-adaptative
unsupervised focal loss to regularize the prediction in the target domain,
which has a mild gradient neutralization mechanism and mitigates the problem
that hard samples are barely optimized in entropy-based methods. In stage two,
we introduce a data augmentation method named cross-domain image mixing (CIM)
to bridge the semantic knowledge from two domains. Our method achieves
state-of-the-art 58.4% and 59.6% mIoUs on SYNTHIA-to-Cityscapes and
GTA5-to-Cityscapes using DeepLabV2 and competitive performance using the
lightweight BiSeNet.
