54 citations · 211 across the 34 of their papers we have counts for
9 papers · 1 filter
Learning a Neural Association Network for Self-supervised Multi-Object Tracking
Shuai Li, Michael Burke, Subramanian Ramamoorthy +1
This paper introduces a novel framework to learn data association for multi-object tracking in a self-supervised manner. Fully-supervised learning methods are known to achieve exce…
Vision-based system identification and 3D keypoint discovery using dynamics constraints
Miguel Jaques, Martin Asenov, Michael Burke +1
This paper introduces V-SysId, a novel method that enables simultaneous keypoint discovery, 3D system identification, and extrinsic camera calibration from an unlabeled video taken…
Learning data association without data association: An EM approach to neural assignment prediction
Michael Burke, Subramanian Ramamoorthy
Data association is a fundamental component of effective multi-object tracking. Current approaches to data-association tend to frame this as an assignment problem relying on gating…
Bias Remediation in Driver Drowsiness Detection systems using Generative Adversarial Networks
Mkhuseli Ngxande, Jules-Raymond Tapamo, Michael Burke
Datasets are crucial when training a deep neural network. When datasets are unrepresentative, trained models are prone to bias because they are unable to generalise to real world s…
Physics-as-Inverse-Graphics: Unsupervised Physical Parameter Estimation from Video
Miguel Jaques, Michael Burke, Timothy Hospedales
We propose a model that is able to perform unsupervised physical parameter estimation of systems from video, where the differential equations governing the scene dynamics are known…
Detecting inter-sectional accuracy differences in driver drowsiness detection algorithms
Mkhuseli Ngxande, Jule-Raymond Tapamo, Michael Burke
Convolutional Neural Networks (CNNs) have been used successfully across a broad range of areas including data mining, object detection, and in business. The dominance of CNNs follo…
DepthwiseGANs: Fast Training Generative Adversarial Networks for Realistic Image Synthesis
Mkhuseli Ngxande, Jules-Raymond Tapamo, Michael Burke
Recent work has shown significant progress in the direction of synthetic data generation using Generative Adversarial Networks (GANs). GANs have been applied in many fields of comp…
Rapid Probabilistic Interest Learning from Domain-Specific Pairwise Image Comparisons
Michael Burke, Siyabonga Mbonambi, Purity Molala +1
A great deal of work aims to discover large general purpose models of image interest or memorability for visual search and information retrieval. This paper argues that image inter…
Single camera pose estimation using Bayesian filtering and Kinect motion priors
Michael Burke, Joan Lasenby
Traditional approaches to upper body pose estimation using monocular vision rely on complex body models and a large variety of geometric constraints. We argue that this is not idea…