Fig. 4
From: Large language model-driven knowledge graph reasoning for enhanced semantic segmentation
Visualization results on the UAVid test set. Each column represents a type of image, with the rows showing different samples. The columns are input images, ground truth, baseline segmentation results, and the segmentation outcomes of our approach, respectively. Compared to DDRNet, our method offers more accurate segmentation results, especially for complex scenes and small objects, by effectively preventing detail loss and segmentation errors.
