103 citations · 130 across the 13 of their papers we have counts for
6 papers · 1 filter
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…
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…
Distinguishing Refracted Features using Light Field Cameras with Application to Structure from Motion
Dorian Tsai, Donald G Dansereau, Thierry Peynot +1
Robots must reliably interact with refractive objects in many applications; however, refractive objects can cause many robotic vision algorithms to become unreliable or even fail,…
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…
Subset Feature Learning for Fine-Grained Category Classification
Zongyuan Ge, Christopher Mccool, Conrad Sanderson +1
Fine-grained categorisation has been a challenging problem due to small inter-class variation, large intra-class variation and low number of training images. We propose a learning…
Modelling Local Deep Convolutional Neural Network Features to Improve Fine-Grained Image Classification
ZongYuan Ge, Chris McCool, Conrad Sanderson +1
We propose a local modelling approach using deep convolutional neural networks (CNNs) for fine-grained image classification. Recently, deep CNNs trained from large datasets have co…