论文标题
双曲线不确定性意识到语义细分
Hyperbolic Uncertainty Aware Semantic Segmentation
论文作者
论文摘要
语义分割（SS）旨在将每个像素分类为预定义的一个类。这项任务在自动驾驶汽车和自动驾驶无人机中起着重要作用。在SS中，许多作品表明，大多数错误分类的像素通常在不确定性高的物体边界附近。但是，现有的SS损失函数不是为处理这些不确定像素的训练过程中的量身定制的，因为这些像素通常被同样地视为确定性分类的像素，并且不能在欧几里得空间中与任意低失真嵌入，从而使SS的性能退化。为了克服这个问题，本文设计了“双曲线不确定性损失”（Hyperul），该论文通过双曲线距离动态强调了双曲线空间中错误分类和高度分类的像素。拟议的Hyperul是模型不可知论，可以轻松地应用于各种神经体系结构。在使用Hyperul到最近的三个SS模型之后，有关CityScapes和Uavid数据集的实验结果表明，现有SS模型的分割性能可以始终如一地提高。
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.
