UNO: Uncertainty-aware Noisy-Or Multimodal Fusion for Unanticipated Input Degradation
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[1] Joydeep Ghosh,et al. On Single Source Robustness in Deep Fusion Models , 2019, NeurIPS.
[2] Yarin Gal,et al. Uncertainty in Deep Learning , 2016 .
[3] Yongtian Wang,et al. Deep Surface Normal Estimation With Hierarchical RGB-D Fusion , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[4] Ashish Kapoor,et al. AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles , 2017, FSR.
[5] Ming Yang,et al. RFBNet: Deep Multimodal Networks with Residual Fusion Blocks for RGB-D Semantic Segmentation , 2019, ArXiv.
[6] Alexander S. Ecker,et al. Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming , 2019, ArXiv.
[7] Roberto Cipolla,et al. Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding , 2015, BMVC.
[8] Max Henrion,et al. Some Practical Issues in Constructing Belief Networks , 1987, UAI.
[9] Zoubin Ghahramani,et al. Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning , 2015, ICML.
[10] Alex Kendall,et al. What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? , 2017, NIPS.
[11] Roland Siegwart,et al. The Fishyscapes Benchmark: Measuring Blind Spots in Semantic Segmentation , 2019, International Journal of Computer Vision.
[12] Samy Bengio,et al. Density estimation using Real NVP , 2016, ICLR.
[13] Youngbae Hwang,et al. Robust Deep Multi-modal Learning Based on Gated Information Fusion Network , 2018, ACCV.
[14] Wolfram Burgard,et al. Multimodal deep learning for robust RGB-D object recognition , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
[15] R. Srikant,et al. Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks , 2017, ICLR.
[16] Wolfram Burgard,et al. Self-Supervised Model Adaptation for Multimodal Semantic Segmentation , 2018, International Journal of Computer Vision.
[17] Kilian Q. Weinberger,et al. On Calibration of Modern Neural Networks , 2017, ICML.
[18] Thomas G. Dietterich,et al. Benchmarking Neural Network Robustness to Common Corruptions and Perturbations , 2018, ICLR.
[19] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[20] Daniel Cremers,et al. FuseNet: Incorporating Depth into Semantic Segmentation via Fusion-Based CNN Architecture , 2016, ACCV.
[21] Zsolt Kira,et al. Fusing LIDAR and images for pedestrian detection using convolutional neural networks , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).
[22] Graham W. Taylor,et al. Deep Multimodal Learning: A Survey on Recent Advances and Trends , 2017, IEEE Signal Processing Magazine.
[23] Wolfram Burgard,et al. AdapNet: Adaptive semantic segmentation in adverse environmental conditions , 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA).
[24] David Heckerman,et al. A New Look at Causal Independence , 1994, UAI.
[25] Jishnu Mukhoti,et al. Evaluating Bayesian Deep Learning Methods for Semantic Segmentation , 2018, ArXiv.
[26] Judea Pearl,et al. Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.
[27] Petros Maragos,et al. Multimodal Fusion and Learning with Uncertain Features Applied to Audiovisual Speech Recognition , 2007, 2007 IEEE 9th Workshop on Multimedia Signal Processing.
[28] Graham W. Taylor,et al. Learning Confidence for Out-of-Distribution Detection in Neural Networks , 2018, ArXiv.
[29] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[30] Wolfram Burgard,et al. Choosing smartly: Adaptive multimodal fusion for object detection in changing environments , 2016, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
[31] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.