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Dimity Miller

11 accepted papers

2026

Changes in Real Time: Online Scene Change Detection with Multi-View Fusion

Chamuditha Jayanga Galappaththige, Jason Lai, Lloyd Windrim, Donald Dansereau, Niko Suenderhauf, Dimity Miller
CVPR 2026

Online Scene Change Detection (SCD) is an extremely challenging problem that requires an agent to detect relevant changes on the fly while observing the scene from unconstrained viewpoints. Existing online SCD methods are significantly less accurate than offline approaches. We present the first onli

Cited by 0Source ↗code ↗Scholar ↗
2025

Backdoor Mitigation via Invertible Pruning Masks

Kealan Dunnett, Reza Arablouei, Volkan Dedeoglu, Dimity Miller, Raja Jurdak
NeurIPS 2025poster

Model pruning has gained traction as a promising defense strategy against backdoor attacks in deep learning. However, existing pruning-based approaches often fall short in accurately identifying and removing the specific parameters responsible for inducing backdoor behaviors. Despite the dominance o…

Cited by 0Source ↗Scholar ↗
2025

Multi-View Pose-Agnostic Change Localization with Zero Labels

Chamuditha Jayanga Galappaththige, Jason Lai, Lloyd Windrim, Donald Dansereau, Niko Sunderhauf, Dimity Miller
CVPR 2025poster

Autonomous agents often require accurate methods for detecting and localizing changes in their environment, particularly when observations are captured from unconstrained and inconsistent viewpoints. We propose a novel label-free, pose-agnostic change detection method that integrates information fro…

Cited by 0Source ↗PDF ↗Scholar ↗
2023

Density-aware NeRF Ensembles: Quantifying Predictive Uncertainty in Neural Radiance Fields

Niko Sünderhauf, Jad Abou-Chakra, Dimity Miller
ICRA 2023poster

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 investigated in prior work simply average rendered RGB images to quantify the model uncertainty caused by conflicting ex…

Cited by 64Source ↗Scholar ↗
2023

Never mind the metrics---what about the uncertainty? Visualising binary confusion matrix metric distributions to put performance in perspective

David Lovell, Dimity Miller, Jaiden Capra, Andrew P. Bradley
ICML 2023poster

There are strong incentives to build classification systems that show outstanding performance on various datasets and benchmarks. This can encourage a narrow focus on models and the performance metrics used to evaluate and compare them—resulting in a growing body of literature to evaluate and compar…

Cited by 3Source ↗PDF ↗Scholar ↗
2023

SAFE: Sensitivity-Aware Features for Out-of-Distribution Object Detection

Samuel Wilson, Tobias Fischer, Feras Dayoub, Dimity Miller, Niko Sünderhauf
ICCV 2023poster

We address the problem of out-of-distribution (OOD) detection for the task of object detection. We show that residual convolutional layers with batch normalisation produce Sensitivity-Aware FEatures (SAFE) that are consistently powerful for distinguishing in-distribution from out-of-distribution det…

Cited by 37PDF ↗code ↗Scholar ↗
2023

Uncertainty-Aware Lidar Place Recognition in Novel Environments

Keita Mason, Joshua Knights, Milad Ramezani, Peyman Moghadam, Dimity Miller
IROS 2023poster

State-of-the-art lidar place recognition models exhibit unreliable performance when tested on environments different from their training dataset, which limits their use in complex and evolving environments. To address this issue, we investigate the task of uncertainty-aware lidar place recognition,…

Cited by 6Source ↗code ↗Scholar ↗
2022

Uncertainty for Identifying Open-Set Errors in Visual Object Detection

Dimity Miller, Niko Sünderhauf, Michael Milford, Feras Dayoub
RA-L 2022

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 real-time method for extracting epistemic uncertainty from object detectors to identify and reject open-set errors. GMM-De

Cited by 54Source ↗code ↗Scholar ↗
2022

What's in the Black Box? The False Negative Mechanisms Inside Object Detectors

Dimity Miller, Peyman Moghadam, Mark Cox, Matt Wildie, Raja Jurdak
RA-L 2022

In object detection, false negatives arise when a detector fails to detect a target object. To understand <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">why</i> object detectors produce false negatives, we identify five ‘false negative mechanisms,’

Cited by 20Source ↗code ↗Scholar ↗
2019

Evaluating Merging Strategies for Sampling-based Uncertainty Techniques in Object Detection

Dimity Miller, Feras Dayoub, Michael Milford, Niko Sünderhauf
ICRA 2019poster

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 semantic segmentation tasks by simply averaging samples, this is not the case for object detection, where detection sam…

Cited by 140Source ↗Scholar ↗
2018

Dropout Sampling for Robust Object Detection in Open-Set Conditions

Dimity Miller, Lachlan Nicholson, Feras Dayoub, Niko Sünderhauf
ICRA 2018poster

Dropout Variational Inference, or Dropout Sampling, has been recently proposed as an approximation technique for Bayesian Deep Learning and evaluated for image classification and regression tasks. This paper investigates the utility of Dropout Sampling for object detection for the first time. We dem…

Cited by 305Source ↗Scholar ↗