no code implementations • 21 Nov 2023 • Jonathan Sadeghi, Nicholas A. Lord, John Redford, Romain Mueller
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.
1 code implementation • ICLR 2022 • 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.
1 code implementation • CVPR 2020 • Luca Bertinetto, Romain Mueller, Konstantinos Tertikas, Sina Samangooei, Nicholas A. Lord
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 the ground truth as equally wrong.
1 code implementation • 29 Oct 2018 • Tommaso Cavallari, Stuart Golodetz, Nicholas A. Lord, Julien Valentin, Victor A. Prisacariu, Luigi Di Stefano, Philip H. S. Torr
The adapted forests achieved relocalisation performance that was on par with that of offline forests, and our approach was able to estimate the camera pose in close to real time.
1 code implementation • NeurIPS 2018 • Saumya Jetley, Nicholas A. Lord, Philip H. S. Torr
Via a novel experimental analysis, we illustrate some facts about deep convolutional networks for image classification that shed new light on their behaviour and how it connects to the problem of adversaries.
4 code implementations • ICLR 2018 • Saumya Jetley, Nicholas A. Lord, Namhoon Lee, Philip H. S. Torr
We propose an end-to-end-trainable attention module for convolutional neural network (CNN) architectures built for image classification.
no code implementations • 25 Jan 2018 • Stuart Golodetz, Tommaso Cavallari, Nicholas A. Lord, Victor A. Prisacariu, David W. Murray, Philip H. S. Torr
Reconstructing dense, volumetric models of real-world 3D scenes is important for many tasks, but capturing large scenes can take significant time, and the risk of transient changes to the scene goes up as the capture time increases.
no code implementations • CVPR 2017 • Tommaso Cavallari, Stuart Golodetz, Nicholas A. Lord, Julien Valentin, Luigi Di Stefano, Philip H. S. Torr
Camera relocalisation is an important problem in computer vision, with applications in simultaneous localisation and mapping, virtual/augmented reality and navigation.