General Semantic Segmentation

General semantic segmentation aims to automatically assign semantic labels to each pixel in an image, creating a pixel-wise classification map. Recent research focuses on improving the robustness and generalization capabilities of segmentation models, particularly addressing challenges like domain adaptation (e.g., adapting models trained on one type of image to another), handling diverse object types (including camouflaged or transparent objects), and efficiently leveraging powerful pre-trained models like SAM. These advancements are crucial for various applications, including remote sensing, medical image analysis, and autonomous systems, where accurate and efficient segmentation is essential for extracting meaningful information.