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The fusion of multiple sensor modalities, especially through deep learning architectures, has been an active area of study.
“Benchmarking Neural Network Robustness to Common Corruptions and Perturbations”
Dan Hendrycks and Thomas Dietterich · 1903
Earlier work this paper cites.
“Some Practical Issues in Constructing Belief Networks.”
Max Henrion · 1987
Earlier work this paper cites.
“A new look at causal independence”
David Heckerman and John Breese · 1994
Earlier work this paper cites.
“Multimodal Fusion and Learning with Uncertain Features Applied to Audiovisual Speech Recognition”
G. Papandreou, A. Katsamanis, V. Pitsikalis and P. Maragos · 2007
Earlier work this paper cites.
“Adam: A Method for Stochastic Optimization”
Diederik. Kingma and Jimmy Ba · 2014
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“Probabilistic reasoning in intelligent systems: networks of plausible inference”
Judea Pearl · 2014
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“Very deep convolutional networks for large-scale image recognition”
Karen Simonyan and Andrew Zisserman · 2014
Earlier work this paper cites.
“Multimodal Deep Learning for Robust RGB-D Object Recognition”
Andreas Eitel et al · 2015
Earlier work this paper cites.
“Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning”
Yarin Gal and Zoubin Ghahramani · 2015
Earlier work this paper cites.
“Deep Residual Learning for Image Recognition”
Kaiming He, Xiangyu Zhang, Shaoqing Ren and Jian Sun · 2015
Earlier work this paper cites.
Alex Kendall, Vijay Badrinarayanan and Roberto Cipolla · 2015
Earlier work this paper cites.
“Fully convolutional networks for semantic segmentation”
Jonathan Long, Evan Shelhamer and Trevor Darrell · 2015
Cited alongside, same era.
“Density estimation using Real NVP”
Laurent Dinh, Jascha Sohl-Dickstein and Samy Bengio · 2016
Cited alongside, same era.
“Uncertainty in deep learning”, 2016
Yarin Gal · 2016
Cited alongside, same era.
“FuseNet: incorporating depth into semantic segmentation via fusion-based CNN architecture”
C. Hazirbas, L. Ma, C. Domokos and D. Cremers · 2016
Cited alongside, same era.
“Choosing smartly: Adaptive multimodal fusion for object detection in changing environments”
Oier Mees, Andreas Eitel and Wolfram Burgard · 2016
Cited alongside, same era.
“Fusing lidar and images for pedestrian detection using convolutional neural networks”
“AdapNet: Adaptive semantic segmentation in adverse environmental conditions”, 2017, pp. 4644–4651
Abhinav Valada, Johan Vertens, Ankit Dhall and Wolfram Burgard · 2017
Later among the works it cites.
“Learning Confidence for Out-of-Distribution Detection in Neural Networks”
Terrance DeVries and Graham. Taylor · 2018
Later among the works it cites.
“Robust Deep Multi-modal Learning Based on Gated Information Fusion Network”
Jaekyum Kim et al · 2018
Later among the works it cites.
“Evaluating Bayesian Deep Learning Methods for Semantic Segmentation”
Jishnu Mukhoti and Yarin Gal · 2018
Later among the works it cites.
“Self-Supervised Model Adaptation for Multimodal Semantic Segmentation”
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Joel Schlosser, Christopher Chow and Zsolt Kira · 2016
Cited alongside, same era.
“On calibration of modern neural networks”
Chuan Guo, Geoff Pleiss, Yu Sun and Kilian Weinberger · 2017
Cited alongside, same era.
“What uncertainties do we need in bayesian deep learning for computer vision?”
Alex Kendall and Yarin Gal · 2017
Cited alongside, same era.
“Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks”
Shiyu Liang, Yixuan Li and R. Srikant · 2017
Cited alongside, same era.
“Deep Multimodal Learning: A Survey on Recent Advances and Trends”
D. Ramachandram and G.. Taylor · 2017
Cited alongside, same era.
“AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles”
Shital Shah, Debadeepta Dey, Chris Lovett and Ashish Kapoor · 2017
Cited alongside, same era.
Abhinav Valada, Rohit Mohan and Wolfram Burgard · 2018
Later among the works it cites.
“The Fishyscapes Benchmark: Measuring Blind Spots in Semantic Segmentation”
Hermann Blum et al · 2019
Closest in time.
“RFBNet: Deep Multimodal Networks with Residual Fusion Blocks for RGB-D Semantic Segmentation”
Liuyuan Deng et al · 2019
Closest in time.
“On Single Source Robustness in Deep Fusion Models”
Taewan Kim and Joydeep Ghosh · 2019
Closest in time.
“Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming”
Claudio Michaelis et al · 2019
Closest in time.
“Deep Surface Normal Estimation with Hierarchical RGB-D Fusion”
Jin Zeng et al · 2019
Closest in time.