Unsupervised Domain Adaptation for Semantic Segmentation via Low-level Edge Information Transfer
This work addresses the domain gap problem in semantic segmentation for computer vision applications, offering an incremental but effective enhancement to existing adaptation techniques.
The paper tackles unsupervised domain adaptation for semantic segmentation by using low-level edge information to guide semantic transfer, achieving state-of-the-art performance on benchmark datasets with concrete improvements over previous methods.
Unsupervised domain adaptation for semantic segmentation aims to make models trained on synthetic data (source domain) adapt to real images (target domain). Previous feature-level adversarial learning methods only consider adapting models on the high-level semantic features. However, the large domain gap between source and target domains in the high-level semantic features makes accurate adaptation difficult. In this paper, we present the first attempt at explicitly using low-level edge information, which has a small inter-domain gap, to guide the transfer of semantic information. To this end, a semantic-edge domain adaptation architecture is proposed, which uses an independent edge stream to process edge information, thereby generating high-quality semantic boundaries over the target domain. Then, an edge consistency loss is presented to align target semantic predictions with produced semantic boundaries. Moreover, we further propose two entropy reweighting methods for semantic adversarial learning and self-supervised learning, respectively, which can further enhance the adaptation performance of our architecture. Comprehensive experiments on two UDA benchmark datasets demonstrate the superiority of our architecture compared with state-of-the-art methods.