Semantic Segmentation Model

Semantic segmentation models aim to assign a semantic label to every pixel in an image, enabling detailed scene understanding. Current research emphasizes improving model robustness against various challenges, including adverse weather conditions, limited labeled data (through techniques like weak supervision and active learning), and adversarial attacks, often leveraging architectures like U-Net and transformers. These advancements are crucial for applications ranging from autonomous driving and robotics to remote sensing and medical image analysis, driving progress in both model efficiency and accuracy.