  Semantic segmentation (SS) aims to classify each pixel into one of the
pre-defined classes. This task plays an important role in self-driving cars and
autonomous drones. In SS, many works have shown that most misclassified pixels
are commonly near object boundaries with high uncertainties. However, existing
SS loss functions are not tailored to handle these uncertain pixels during
training, as these pixels are usually treated equally as confidently classified
pixels and cannot be embedded with arbitrary low distortion in Euclidean space,
thereby degenerating the performance of SS. To overcome this problem, this
paper designs a "Hyperbolic Uncertainty Loss" (HyperUL), which dynamically
highlights the misclassified and high-uncertainty pixels in Hyperbolic space
during training via the hyperbolic distances. The proposed HyperUL is model
agnostic and can be easily applied to various neural architectures. After
employing HyperUL to three recent SS models, the experimental results on
Cityscapes and UAVid datasets reveal that the segmentation performance of
existing SS models can be consistently improved.
