Figure 1. Two examples of insights that come for free with Hyperbolic Image Segmentation. For both examples, each black dot denotes a pixel embedding in hyperbolic space. Left (Pascal VOC): next to per-pixel classification, the distance to the origin in hyperbolic space provides a free measure of uncertainty. Right (COCO-Stuff-10k): the hyperbolic positioning of pixels even allows us to pinpoint interiors and edges of objects, as indicated by the colored boxes and their corresponding pixels in the segmentation map. Other benefits of hyperbolic embeddings for segmentation include zero-label generalization and better performance in low-dimensional embedding spaces.