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Found 8 papers, 5 papers with code
Attacking Motion Planners Using Adversarial Perception Errors
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.
Attacking deep networks with surrogate-based adversarial black-box methods is easy
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
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.
Real-Time RGB-D Camera Pose Estimation in Novel Scenes using a Relocalisation Cascade
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.
With Friends Like These, Who Needs Adversaries?
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.
Learn To Pay Attention
We propose an end-to-end-trainable attention module for convolutional neural network (CNN) architectures built for image classification.
Collaborative Large-Scale Dense 3D Reconstruction with Online Inter-Agent Pose Optimisation
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.
On-the-Fly Adaptation of Regression Forests for Online Camera Relocalisation
Camera relocalisation is an important problem in computer vision, with applications in simultaneous localisation and mapping, virtual/augmented reality and navigation.
