While a number of promising uncertainty quantification methods have been proposed to address the prevailing shortcomings of deep neural networks like overconfidence and lack of explainability, quantifying predictive uncertainties in the context of joint semantic segmentation and monocular depth estimation has not been explored yet. Since many real-world applications are multi-modal in nature and, hence, have the potential to benefit from multi-task learning, this is a substantial gap in current literature. To this end, we conduct a comprehensive series of experiments to study how multi-task learning influences the quality of uncertainty estimates in comparison to solving both tasks separately.

翻译：尽管已有许多有前景的不确定性量化方法被提出，以解决深度神经网络普遍存在的过度自信和可解释性不足等缺陷，但在联合语义分割与单目深度估计的背景下量化预测不确定性尚未得到探索。由于许多实际应用本质上是多模态的，因而可能从多任务学习中受益，这是当前文献中的一个显著空白。为此，我们进行了一系列综合性实验，以研究多任务学习如何影响不确定性估计的质量，并与分别解决这两个任务的情况进行比较。