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.

翻译：语义分割（SS）旨在将每个像素分类到预定义的类别之一。该任务在自动驾驶汽车和自主无人机中发挥着重要作用。在语义分割中，许多研究表明，大部分被错误分类的像素通常位于具有高不确定性的物体边界附近。然而，现有的语义分割损失函数在设计上并未针对训练过程中处理这些不确定像素进行优化，因为这类像素通常与高置信度分类像素被同等对待，且在欧几里得空间中难以实现低失真嵌入，从而降低了语义分割的性能。为解决这一问题，本文设计了一种"双曲不确定性损失"（HyperUL），该损失通过双曲距离在训练过程中动态突出双曲空间中的错误分类和高不确定性像素。所提出的HyperUL具有模型无关性，可轻松应用于各类神经网络架构。将HyperUL应用于三种最新的语义分割模型后，在Cityscapes和UAVid数据集上的实验结果表明，现有语义分割模型的分割性能能够得到持续提升。