10 citations · 12 across the 7 of their papers we have counts for
8 papers
MObI: Multimodal Object Inpainting Using Diffusion Models
Alexandru Buburuzan, Anuj Sharma, John Redford +2
Safety-critical applications, such as autonomous driving, require extensive multimodal data for rigorous testing. Methods based on synthetic data are gaining prominence due to the…
Lift-Attend-Splat: Bird's-eye-view camera-lidar fusion using transformers
James Gunn, Zygmunt Lenyk, Anuj Sharma +4
Combining complementary sensor modalities is crucial to providing robust perception for safety-critical robotics applications such as autonomous driving (AD). Recent state-of-the-a…
Attacking Motion Planners Using Adversarial Perception Errors
Jonathan Sadeghi, Nicholas A. Lord, John Redford +1
Autonomous driving (AD) systems are often built and tested in a modular fashion, where the performance of different modules is measured using task-specific metrics. These metrics s…
An Active Learning Reliability Method for Systems with Partially Defined Performance Functions
Jonathan Sadeghi, Romain Mueller, John Redford
In engineering design, one often wishes to calculate the probability that the performance of a system is satisfactory under uncertainty. State of the art algorithms exist to solve…
Query-based Hard-Image Retrieval for Object Detection at Test Time
Edward Ayers, Jonathan Sadeghi, John Redford +2
There is a longstanding interest in capturing the error behaviour of object detectors by finding images where their performance is likely to be unsatisfactory. In real-world applic…
Attacking deep networks with surrogate-based adversarial black-box methods is easy
Nicholas A. Lord, Romain Mueller, Luca Bertinetto
A recent line of work on black-box adversarial attacks has revived the use of transfer from surrogate models by integrating it into query-based search. However, we find that existi…
Parameter-free Online Test-time Adaptation
Malik Boudiaf, Romain Mueller, Ismail Ben Ayed +1
Training state-of-the-art vision models has become prohibitively expensive for researchers and practitioners. For the sake of accessibility and resource reuse, it is important to f…
Making Better Mistakes: Leveraging Class Hierarchies with Deep Networks
Luca Bertinetto, Romain Mueller, Konstantinos Tertikas +2
Deep neural networks have improved image classification dramatically over the past decade, but have done so by focusing on performance measures that treat all classes other than th…