DSP: Dual Soft-Paste for Unsupervised Domain Adaptive Semantic Segmentation

Understand

Unsupervised domain adaptation (UDA) for semantic segmentation aims to adapt a segmentation model trained on the labeled source domain to the unlabeled target domain.

• Existing methods try to learn domain invariant features while suffering from large domain gaps that make it difficult to correctly align discrepant features, especially in the initial training phase.
• To address this issue, we propose a novel Dual Soft-Paste (DSP) method in this paper.
• Specifically, DSP selects some classes from a source domain image using a long-tail class first sampling strategy and softly pastes the corresponding image patch on both the source and target training images with a fusion weight.