Hierarchical Semantic Segmentation

Hierarchical semantic segmentation aims to create image segmentations at multiple levels of detail, reflecting the inherent hierarchical structure of object categories (e.g., "car" encompassing "wheel" and "door"). Current research focuses on developing models that effectively capture these relationships, exploring both hierarchical and "flat" architectures (where higher-level information is inferred from lower-level details) within various geometric spaces like Euclidean and hyperbolic spaces. This work is significant for improving the accuracy and robustness of semantic segmentation, particularly in applications like scene understanding, object recognition, and damage detection in infrastructure monitoring where nuanced detail is crucial.