Contextrast++: Robust Multi-Scale Contextual Contrastive Learning for Semantic Segmentation

Area of Science:

• Computer Vision
• Deep Learning
• Machine Learning

Background:

• Deep learning has advanced semantic segmentation.
• Challenges persist in capturing local/global contexts and handling long-tailed distributions.
• Existing methods struggle with multi-scale feature integration and class imbalance.

Purpose of the Study:

• To introduce Contextrast++, a novel contrastive learning method for semantic segmentation.
• To improve multi-scale feature integration and mitigate class imbalance issues.
• To enhance segmentation precision and context-aware representations.

Main Methods:

• Contextual Contrastive Learning (CCL) with an adaptive fusion module, pixel-to-anchor (PA) loss, and anchor-to-anchor (AA) loss.
• Boundary-Aware Negative (BANE) sampling for refined boundary details.
• Utilizing a memory bank for class-balanced anchors to address long-tailed distributions.

Main Results:

• Contextrast++ substantially improves semantic segmentation performance.
• Achieved superior results over existing contrastive learning-based state-of-the-art approaches.
• Demonstrated effectiveness on public datasets.

Conclusions:

• Contextrast++ offers a robust solution for semantic segmentation challenges.
• The method effectively integrates multi-scale features and addresses class imbalance.
• Contextrast++ provides significant performance gains without additional inference overhead.