  The fusion of multiple sensor modalities, especially through deep learning
architectures, has been an active area of study. However, an under-explored
aspect of such work is whether the methods can be robust to degradations across
their input modalities, especially when they must generalize to degradations
not seen during training. In this work, we propose an uncertainty-aware fusion
scheme to effectively fuse inputs that might suffer from a range of known and
unknown degradations. Specifically, we analyze a number of uncertainty
measures, each of which captures a different aspect of uncertainty, and we
propose a novel way to fuse degraded inputs by scaling modality-specific output
softmax probabilities. We additionally propose a novel data-dependent spatial
temperature scaling method to complement these existing uncertainty measures.
Finally, we integrate the uncertainty-scaled output from each modality using a
probabilistic noisy-or fusion method. In a photo-realistic simulation
environment (AirSim), we show that our method achieves significantly better
results on a semantic segmentation task, compared to state-of-art fusion
architectures, on a range of degradations (e.g. fog, snow, frost, and various
other types of noise), some of which are unknown during training. We
specifically improve upon the state-of-art[1] by 28% in mean IoU on various
degradations. [1] Abhinav Valada, Rohit Mohan, and Wolfram Burgard.
Self-Supervised Model Adaptation for Multimodal Semantic Segmentation. In:
arXiv e-prints, arXiv:1808.03833 (Aug. 2018), arXiv:1808.03833. arXiv:
1808.03833 [cs.CV].
