Search Results for author: Nicholas A. Lord

Found 7 papers, 5 papers with code

Attacking deep networks with surrogate-based adversarial black-box methods is easy

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.

Making Better Mistakes: Leveraging Class Hierarchies with Deep Networks

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.

Image Classification

Real-Time RGB-D Camera Pose Estimation in Novel Scenes using a Relocalisation Cascade

1 code implementation29 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.

Pose Estimation

With Friends Like These, Who Needs Adversaries?

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.

Adversarial Attack Classification +2

Learn To Pay Attention

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.

Adversarial Attack General Classification +3

Collaborative Large-Scale Dense 3D Reconstruction with Online Inter-Agent Pose Optimisation

no code implementations25 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.

3D Reconstruction

On-the-Fly Adaptation of Regression Forests for Online Camera Relocalisation

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.

Camera Relocalization

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