Supervised Semantic Segmentation

Supervised semantic segmentation aims to assign a semantic label to each pixel in an image, enabling detailed scene understanding. Current research emphasizes reducing the need for extensive pixel-level annotations by exploring weakly-supervised and self-supervised learning techniques, often employing U-Net architectures or transformers, and leveraging contrastive learning or self-play algorithms to improve performance. These advancements are crucial for applications requiring large-scale segmentation, such as medical image analysis and autonomous driving, where fully-labeled datasets are often unavailable or prohibitively expensive to create. The development of more efficient and robust segmentation methods is driving significant progress in various fields.