Softmax函数
计算机科学
保险丝(电气)
人工智能
概率逻辑
模态(人机交互)
航程(航空)
噪音(视频)
机器学习
模式
降级(电信)
传感器融合
融合
补语(音乐)
深度学习
图像(数学)
工程类
哲学
社会学
航空航天工程
表型
电气工程
化学
互补
基因
电信
生物化学
语言学
社会科学
作者
Junjiao Tian,Wesley Cheung,Nathaniel Glaser,Yen‐Cheng Liu,Zsolt Kira
标识
DOI:10.1109/icra40945.2020.9197266
摘要
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 degradation across their input modalities, especially when they must generalize to degradation 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 degradation. 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, as compared to state-of-art fusion architectures, on a range of degradation (e.g. fog, snow, frost, and various other types of noise), some of which are unknown during training.
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