Efficient Multi-task Uncertainties for Joint Semantic Segmentation and
  Monocular Depth Estimation
Steven Landgraf Markus Hillemann Theodor Kapler Markus Ulrich
Quantifying the predictive uncertainty emerged as a possible solution to
common challenges like overconfidence or lack of explainability and robustness
of deep neural networks, albeit one that is often computationally expensive.
Many real-world applications are multi-modal in nature and hence benefit from
multi-task learning. In autonomous driving, for example, the joint solution of
semantic segmentation and monocular depth estimation has proven to be valuable.
In this work, we first combine different uncertainty quantification methods
with joint semantic segmentation and monocular depth estimation and evaluate
how they perform in comparison to each other. Additionally, we reveal the
benefits of multi-task learning with regard to the uncertainty quality compared
to solving both tasks separately. Based on these insights, we introduce
EMUFormer, a novel student-teacher distillation approach for joint semantic
segmentation and monocular depth estimation as well as efficient multi-task
uncertainty quantification. By implicitly leveraging the predictive
uncertainties of the teacher, EMUFormer achieves new state-of-the-art results
on Cityscapes and NYUv2 and additionally estimates high-quality predictive
uncertainties for both tasks that are comparable or superior to a Deep Ensemble
despite being an order of magnitude more efficient.