  Semantic segmentation is an important technique for environment perception in
intelligent transportation systems. With the rapid development of convolutional
neural networks (CNNs), road scene analysis can usually achieve satisfactory
results in the source domain. However, guaranteeing good generalization to
different target domain scenarios remains a significant challenge. Recently,
semi-supervised learning and active learning have been proposed to alleviate
this problem. Semisupervised learning can improve model accuracy with massive
unlabeled data, but some pseudo labels containing noise would be generated with
limited or imbalanced training data. And there will be suboptimal models if
human guidance is absent. Active learning can select more effective data to
intervene, while the model accuracy can not be improved because the massive
unlabeled data are not used. And the probability of querying sub-optimal
samples will increase when the domain difference is too large, increasing
annotation cost. This paper proposes an iterative loop method combining active
and semisupervised learning for domain adaptive semantic segmentation. The
method first uses semi-supervised to learn massive unlabeled data to improve
model accuracy and provide more accurate selection models for active learning.
Secondly, combined with the predictive uncertainty sample selection strategy of
active learning, manual intervention is used to correct the pseudo-labels.
Finally, flexible iterative loops achieve the best performance with minimal
labeling cost. Extensive experiments show that our method establishes
state-of-the-art performance on tasks of GTAV to Cityscapes, SYNTHIA to
Cityscapes, improving by 4.9% mIoU and 5.2% mIoU, compared to the previous best
method, respectively.
