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SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
We present a novel and practical deep fully convolutional neural network architecture for semantic pixel-wise segmentation termed SegNet. This core trainable segmentation engine consists of anExpand
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PoseNet: A Convolutional Network for Real-Time 6-DOF Camera Relocalization
We present a robust and real-time monocular six degree of freedom relocalization system. Our system trains a convolutional neural network to regress the 6-DOF camera pose from a single RGB image inExpand
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What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?
There are two major types of uncertainty one can model. Aleatoric uncertainty captures noise inherent in the observations. On the other hand, epistemic uncertainty accounts for uncertainty in theExpand
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End-to-End Learning of Geometry and Context for Deep Stereo Regression
We propose a novel deep learning architecture for regressing disparity from a rectified pair of stereo images. We leverage knowledge of the problem’s geometry to form a cost volume using deep featureExpand
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Multi-task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics
Numerous deep learning applications benefit from multitask learning with multiple regression and classification objectives. In this paper we make the observation that the performance of such systemsExpand
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Geometric Loss Functions for Camera Pose Regression with Deep Learning
Deep learning has shown to be effective for robust and real-time monocular image relocalisation. In particular, PoseNet [22] is a deep convolutional neural network which learns to regress the 6-DOFExpand
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Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding
We present a deep learning framework for probabilistic pixel-wise semantic segmentation, which we term Bayesian SegNet. Semantic segmentation is an important tool for visual scene understanding and aExpand
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Modelling uncertainty in deep learning for camera relocalization
  • Alex Kendall, R. Cipolla
  • Engineering, Computer Science
  • IEEE International Conference on Robotics and…
  • 19 September 2015
We present a robust and real-time monocular six degree of freedom visual relocalization system. We use a Bayesian convolutional neural network to regress the 6-DOF camera pose from a single RGBExpand
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Concrete Dropout
Dropout is used as a practical tool to obtain uncertainty estimates in large vision models and reinforcement learning (RL) tasks. But to obtain well-calibrated uncertainty estimates, a grid-searchExpand
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Concrete Problems for Autonomous Vehicle Safety: Advantages of Bayesian Deep Learning
Autonomous vehicle (AV) software is typically composed of a pipeline of individual components, linking sensor inputs to motor outputs. Erroneous component outputs propagate downstream, hence safe AVExpand
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