2D Semantic Segmentation: Recent Developments and Future Directions

Semantic segmentation is a critical task in computer vision that aims to assign each pixel an image corresponding label on the basis of its semantic content. This commonly referred as dense labeling because it requires pixel-level classification image. The research area vast and has achieved advances recent years. Deep learning architectures particular have shown remarkable performance generating high-level, hierarchical, features from images. Among these architectures, convolutional neural networks been widely used address problems. work review analyze technological developments segmentation. It provides overview traditional deep-learning-based approaches analyzes their structural characteristics, strengths, limitations. Specifically, focuses technical 2D methods proposed over past decade discusses current challenges future development direction potential areas need further exploration are also examined.