A Prototypical Knowledge Oriented Adaptation Framework for Semantic Segmentation.

Area of Science:

• Computer Vision
• Machine Learning
• Artificial Intelligence

Background:

• Supervised semantic segmentation requires extensive labeled data and struggles with cross-domain generalization.
• Existing domain adaptation methods can lead to over-adaptation on target domains due to intra-domain divergence.
• Output-space adaptation shows promise but needs refinement for nuanced domain discrepancy.

Purpose of the Study:

• To develop a domain adaptation technique for semantic segmentation that mitigates over-adaptation.
• To improve cross-domain invariant representation learning by leveraging target domain knowledge.
• To enhance the robustness and accuracy of semantic segmentation models in new domains.

Main Methods:

• Leveraging prototypical knowledge to create a continuous domain space for pixel-wise adaptation.
• Employing a soft adversarial loss for domain discrepancy alleviation.
• Utilizing a unilateral discriminator to reduce uncertainty in prototypical knowledge.

Main Results:

• The proposed method effectively guides distribution alignment and reduces over-adaptation.
• Demonstrated competitive performance against state-of-the-art methods on two cross-domain segmentation tasks.
• Prototypical knowledge enables tailored adaptation strategies for different regions within the target domain.

Conclusions:

• The prototypical knowledge-oriented adaptation approach offers effective cross-domain generalization for semantic segmentation.
• The method successfully addresses limitations of previous output-space adaptation techniques.
• This work provides a robust framework for improving deep learning model performance across different data domains.