Unsupervised domain adaptive segmentation algorithm based on two-level category alignment

Abstract

To enhance the model’s generalization ability in unsupervised domain adaptive segmentation tasks, most approaches have primarily focused on pixel-level local features, but neglected the clue in category information. This limitation results in the segmentation network only learning global inter-domain invariant features but ignoring the category-specific inter-domain invariant features, which degenerates the segmentation performance. To address this issue, we present an Unsupervised Domain Adaptive algorithm based on two-level Category Alignment in two different spaces for semantic segmentation tasks, denoted as UDAca+. The first level is image-level category alignment based on class activation map (CAM), and the second one is pixel-level category alignment based on pseudo label. By utilizing category information, UDAca+ can effectively capture domain-invariant yet category-discriminative feature representations to improve segmentation accuracy. In addition, an adversarial learning-based strategy in mixed domain is designed to train the proposed network. Moreover, a confidence calculation method is introduced to mitigate the misleading issues of negative transfer and over-alignment caused by the noise in image-level pseudo labels. UDAca+ achieves the state-of-the-art (SOTA) performance on two synthetic-to-real adaptative tasks, and verifies its effectiveness for image segmentation.