98 citations · 390 across the 26 of their papers we have counts for
12 papers · 1 filter
The Need for Inherently Privacy-Preserving Vision in Trustworthy Autonomous Systems
Adam K. Taras, Niko Suenderhauf, Peter Corke +1
Vision is a popular and effective sensor for robotics from which we can derive rich information about the environment: the geometry and semantics of the scene, as well as the age,…
Density-aware NeRF Ensembles: Quantifying Predictive Uncertainty in Neural Radiance Fields
Niko Sünderhauf, Jad Abou-Chakra, Dimity Miller
We show that ensembling effectively quantifies model uncertainty in Neural Radiance Fields (NeRFs) if a density-aware epistemic uncertainty term is considered. The naive ensembles…
Critic Guided Segmentation of Rewarding Objects in First-Person Views
Andrew Melnik, Augustin Harter, Christian Limberg +3
This work discusses a learning approach to mask rewarding objects in images using sparse reward signals from an imitation learning dataset. For that, we train an Hourglass network…
Probabilistic Visual Place Recognition for Hierarchical Localization
Ming Xu, Niko Sünderhauf, Michael Milford
Visual localization techniques often comprise a hierarchical localization pipeline, with a visual place recognition module used as a coarse localizer to initialize a pose refinemen…
Did You Miss the Sign? A False Negative Alarm System for Traffic Sign Detectors
Quazi Marufur Rahman, Niko Sünderhauf, Feras Dayoub
Object detection is an integral part of an autonomous vehicle for its safety-critical and navigational purposes. Traffic signs as objects play a vital role in guiding such systems.…
Probabilistic Object Detection: Definition and Evaluation
David Hall, Feras Dayoub, John Skinner +6
We introduce Probabilistic Object Detection, the task of detecting objects in images and accurately quantifying the spatial and semantic uncertainties of the detections. Given the…
Evaluating Merging Strategies for Sampling-based Uncertainty Techniques in Object Detection
Dimity Miller, Feras Dayoub, Michael Milford +1
There has been a recent emergence of sampling-based techniques for estimating epistemic uncertainty in deep neural networks. While these methods can be applied to classification or…
Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environments
Peter Anderson, Qi Wu, Damien Teney +6
A robot that can carry out a natural-language instruction has been a dream since before the Jetsons cartoon series imagined a life of leisure mediated by a fleet of attentive robot…
Dropout Sampling for Robust Object Detection in Open-Set Conditions
Dimity Miller, Lachlan Nicholson, Feras Dayoub +1
Dropout Variational Inference, or Dropout Sampling, has been recently proposed as an approximation technique for Bayesian Deep Learning and evaluated for image classification and r…
SceneCut: Joint Geometric and Object Segmentation for Indoor Scenes
Trung Pham, Thanh-Toan Do, Niko Sünderhauf +1
This paper presents SceneCut, a novel approach to jointly discover previously unseen objects and non-object surfaces using a single RGB-D image. SceneCut's joint reasoning over sce…
Episode-Based Active Learning with Bayesian Neural Networks
Feras Dayoub, Niko Sünderhauf, Peter Corke
We investigate different strategies for active learning with Bayesian deep neural networks. We focus our analysis on scenarios where new, unlabeled data is obtained episodically, s…
Deep Learning Features at Scale for Visual Place Recognition
Zetao Chen, Adam Jacobson, Niko Sunderhauf +5
The success of deep learning techniques in the computer vision domain has triggered a range of initial investigations into their utility for visual place recognition, all using gen…