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Domain Adaptative Semantic Segmentation by alleviating Long-tail Problem

The domain adaptive method based on the adversarial network can be effectively applied to unsupervised semantic segmentation tasks. State-of-the-art approaches have proved that domain alignment at the semantic level can improve segmentation networks' performance. Based on data observation between different domains, we find that the long-tail problem exists in these datasets. We propose a two-level class balancing model to alleviate the semantic class imbalance of data to address this problem. Specifically, we count the category frequencies in the datasets and treat this frequency information as mutual information. Then, we feed this mutual information to the cross-entropy method for fitting so that the model can alleviate the long-tail problem globally. Besides, we resample the data in the model's training process by using two classifiers to balance the head class and the tail class locally. Finally, we use self-supervised learning to supervise the target domain's alignment and the source domain, thus achieving further improvement. We conduct experiments on the benchmark of mainstream unsupervised domain adaptive semantic segmentation tasks, and the experimental results show that our proposed method is effective.