Segmentation Head

A segmentation head is a neural network component that refines feature maps from a backbone network to produce pixel-wise segmentation masks, crucial for tasks like object localization and semantic segmentation in images and videos. Current research focuses on improving segmentation head performance through various strategies, including incorporating uncertainty estimates for robust predictions in federated learning settings, designing specialized architectures like dual-branch heads for enhanced feature extraction (e.g., handling high-frequency signals), and leveraging self-supervised learning or shape priors to address imperfect or limited annotations. These advancements are significantly impacting fields such as medical image analysis, autonomous driving, and document processing by enabling more accurate and efficient object segmentation from diverse data sources.