[HTML][HTML] A review of uncertainty quantification in deep learning: Techniques, applications and challenges

M Abdar, F Pourpanah, S Hussain, D Rezazadegan… - Information fusion, 2021 - Elsevier
Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …

A review and comparative study on probabilistic object detection in autonomous driving

D Feng, A Harakeh, SL Waslander… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Capturing uncertainty in object detection is indispensable for safe autonomous driving. In
recent years, deep learning has become the de-facto approach for object detection, and …

Uncertainty as a form of transparency: Measuring, communicating, and using uncertainty

U Bhatt, J Antorán, Y Zhang, QV Liao… - Proceedings of the …, 2021 - dl.acm.org
Algorithmic transparency entails exposing system properties to various stakeholders for
purposes that include understanding, improving, and contesting predictions. Until now, most …

Artificial intelligence (AI) in augmented reality (AR)-assisted manufacturing applications: a review

CK Sahu, C Young, R Rai - International Journal of Production …, 2021 - Taylor & Francis
Augmented reality (AR) has proven to be an invaluable interactive medium to reduce
cognitive load by bridging the gap between the task-at-hand and relevant information by …

Understanding the limitations of cnn-based absolute camera pose regression

T Sattler, Q Zhou, M Pollefeys… - Proceedings of the …, 2019 - openaccess.thecvf.com
Visual localization is the task of accurate camera pose estimation in a known scene. It is a
key problem in computer vision and robotics, with applications including self-driving cars …

Relpose: Predicting probabilistic relative rotation for single objects in the wild

JY Zhang, D Ramanan, S Tulsiani - European Conference on Computer …, 2022 - Springer
We describe a data-driven method for inferring the camera viewpoints given multiple images
of an arbitrary object. This task is a core component of classic geometric pipelines such as …

Geometric loss functions for camera pose regression with deep learning

A Kendall, R Cipolla - … of the IEEE conference on computer …, 2017 - openaccess.thecvf.com
Deep learning has shown to be effective for robust and real-time monocular image
relocalisation. In particular, PoseNet is a deep convolutional neural network which learns to …

Demon: Depth and motion network for learning monocular stereo

B Ummenhofer, H Zhou, J Uhrig… - Proceedings of the …, 2017 - openaccess.thecvf.com
In this paper we formulate structure from motion as a learning problem. We train a
convolutional network end-to-end to compute depth and camera motion from successive …

Self-supervised learning with geometric constraints in monocular video: Connecting flow, depth, and camera

Y Chen, C Schmid… - Proceedings of the IEEE …, 2019 - openaccess.thecvf.com
We present GLNet, a self-supervised framework for learning depth, optical flow, camera
pose and intrinsic parameters from monocular video--addressing the difficulty of acquiring …

Posenet: A convolutional network for real-time 6-dof camera relocalization

A Kendall, M Grimes, R Cipolla - Proceedings of the IEEE …, 2015 - openaccess.thecvf.com
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 …