Semi-Supervised Semantic Segmentation with Pixel-Level Contrastive Learning from a Class-wise Memory Bank

Understand

This work presents a novel approach for semi-supervised semantic segmentation.

• The key element of this approach is our contrastive learning module that enforces the segmentation network to yield similar pixel-level feature representations for same-class samples across the whole dataset.
• To achieve this, we maintain a memory bank continuously updated with relevant and high-quality feature vectors from labeled data.
• In an end-to-end training, the features from both labeled and unlabeled data are optimized to be similar to same-class samples from the memory bank.