PiPa: Pixel- and Patch-wise Self-supervised Learning for Domain Adaptative Semantic Segmentation

Understand

Unsupervised Domain Adaptation (UDA) aims to enhance the generalization of the learned model to other domains.

• The domain-invariant knowledge is transferred from the model trained on labeled source domain, e.g., video game, to unlabeled target domains, e.g., real-world scenarios, saving annotation expenses.
• Existing UDA methods for semantic segmentation usually focus on minimizing the inter-domain discrepancy of various levels, e.g., pixels, features, and predictions, for extracting domain-invariant knowledge.
• However, the primary intra-domain knowledge, such as context correlation inside an image, remains underexplored.