A simple preprocessing approach for improving semantic segmentation in unsupervised domain adaptation.

Area of Science:

• Computer Vision
• Machine Learning
• Artificial Intelligence

Background:

• Unsupervised Domain Adaptation (UDA) is crucial for applying models trained on synthetic data to real-world scenarios.
• Manual annotation of real-world data is expensive and time-consuming, limiting the scalability of supervised learning.
• Bridging the domain gap between synthetic (source) and real-world (target) data is a key challenge in UDA.

Purpose of the Study:

• To introduce ProCST, a novel preprocessing framework for Unsupervised Domain Adaptation (UDA).
• To translate source images into target-like images while preserving semantic content for improved model training.
• To enhance existing UDA pipelines by reducing the domain gap and improving performance in semantic segmentation tasks.

Main Methods:

• ProCST employs a multi-scale architecture for image translation.
• A unique combination of losses, including a cyclic label loss, is utilized to maintain semantic structure and context.
• The framework is designed as a preprocessing stage, seamlessly integrating into existing UDA pipelines.

Main Results:

• ProCST significantly reduces the domain gap between synthetic and real-world data.
• The method achieves consistent performance gains in semantic segmentation tasks.
• Improvements of up to 1.1% mIoU on GTA5 → Cityscapes and 2.2% on an industrial waste segmentation challenge were observed, surpassing current state-of-the-art results.

Conclusions:

• ProCST effectively generates target-like images with high semantic fidelity, suitable for robust model training.
• The framework offers a cost-effective solution for domain adaptation in semantic segmentation.
• ProCST facilitates the advancement of real-world applications that depend on large-scale annotated data.