Semantic Segmentation Algorithm

Semantic segmentation algorithms aim to assign a class label to every pixel in an image, enabling detailed scene understanding. Current research emphasizes improving robustness and efficiency, focusing on techniques like weakly-supervised learning (using image-level or point-level labels instead of full pixel-level annotations), handling class and size imbalances in datasets, and incorporating confidence assessments into model outputs. These advancements are crucial for various applications, including medical image analysis, remote sensing, autonomous driving, and industrial automation, where accurate and efficient segmentation is essential.