134 citations · 725 across the 66 of their papers we have counts for
8 papers · 1 filter
Hyperdimensional Feature Fusion for Out-Of-Distribution Detection
Samuel Wilson, Tobias Fischer, Niko Sünderhauf +1
We introduce powerful ideas from Hyperdimensional Computing into the challenging field of Out-of-Distribution (OOD) detection. In contrast to most existing work that performs OOD d…
FSNet: A Failure Detection Framework for Semantic Segmentation
Quazi Marufur Rahman, Niko Sünderhauf, Peter Corke +1
Semantic segmentation is an important task that helps autonomous vehicles understand their surroundings and navigate safely. During deployment, even the most mature segmentation mo…
Evaluating the Impact of Semantic Segmentation and Pose Estimation on Dense Semantic SLAM
Suman Raj Bista, David Hall, Ben Talbot +3
Recent Semantic SLAM methods combine classical geometry-based estimation with deep learning-based object detection or semantic segmentation. In this paper we evaluate the quality o…
Going Deeper into Semi-supervised Person Re-identification
Olga Moskvyak, Frederic Maire, Feras Dayoub +1
Person re-identification is the challenging task of identifying a person across different camera views. Training a convolutional neural network (CNN) for this task requires annotat…
Uncertainty for Identifying Open-Set Errors in Visual Object Detection
Dimity Miller, Niko Sünderhauf, Michael Milford +1
Deployed into an open world, object detectors are prone to open-set errors, false positive detections of object classes not present in the training dataset. We propose GMM-Det, a r…
Semi-supervised Keypoint Localization
Olga Moskvyak, Frederic Maire, Feras Dayoub +1
Knowledge about the locations of keypoints of an object in an image can assist in fine-grained classification and identification tasks, particularly for the case of objects that ex…
Semantics for Robotic Mapping, Perception and Interaction: A Survey
Sourav Garg, Niko Sünderhauf, Feras Dayoub +9
For robots to navigate and interact more richly with the world around them, they will likely require a deeper understanding of the world in which they operate. In robotics and rela…
Run-Time Monitoring of Machine Learning for Robotic Perception: A Survey of Emerging Trends
Quazi Marufur Rahman, Peter Corke, Feras Dayoub
As deep learning continues to dominate all state-of-the-art computer vision tasks, it is increasingly becoming an essential building block for robotic perception. This raises impor…