7 citations · 7 across the 1 of their papers we have counts for
5 papers · 1 filter
Open-Set Recognition in the Age of Vision-Language Models
Dimity Miller, Niko Sünderhauf, Alex Kenna +1
Are vision-language models (VLMs) for open-vocabulary perception inherently open-set models because they are trained on internet-scale datasets? We answer this question with a clea…
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…
Class Anchor Clustering: a Loss for Distance-based Open Set Recognition
Dimity Miller, Niko Sünderhauf, Michael Milford +1
In open set recognition, deep neural networks encounter object classes that were unknown during training. Existing open set classifiers distinguish between known and unknown classe…
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…