Semantic Segmentation Network

Semantic segmentation networks aim to assign a semantic label to each pixel in an image, enabling precise scene understanding. Current research focuses on improving accuracy and efficiency, particularly for challenging scenarios like anomaly detection, large-scale 3D point clouds, and domain generalization, often employing Vision Transformers, U-Net architectures, and contrastive learning methods. These advancements are crucial for applications ranging from autonomous driving and medical image analysis to remote sensing and industrial automation, where accurate pixel-level classification is essential.