Dense Label

Dense labeling, the process of assigning labels to every data point (e.g., pixel, voxel, or time step), is a crucial but computationally expensive task across various fields like image segmentation, 3D scene completion, and activity recognition. Current research focuses on mitigating this cost through techniques like sparse annotation strategies combined with generative models (e.g., cGANs) or pseudo-label generation from pre-trained models (e.g., CLIP), often employing architectures such as U-Net and graph convolutional networks. These advancements aim to improve the efficiency and accuracy of dense labeling, impacting applications ranging from autonomous driving and medical image analysis to improved music transcription and animal behavior understanding.