25 citations · 35 across the 4 of their papers we have counts for
9 papers · 1 filter
Bayesian uncertainty-weighted loss for improved generalisability on polyp segmentation task
Rebecca S. Stone, Pedro E. Chavarrias-Solano, Andrew J. Bulpitt +2
While several previous studies have devised methods for segmentation of polyps, most of these methods are not rigorously assessed on multi-center datasets. Variability due to appea…
A Horse with no Labels: Self-Supervised Horse Pose Estimation from Unlabelled Images and Synthetic Prior
Jose Sosa, David Hogg
Obtaining labelled data to train deep learning methods for estimating animal pose is challenging. Recently, synthetic data has been widely used for pose estimation tasks, but most…
3D shape reconstruction of semi-transparent worms
Thomas P. Ilett, Omer Yuval, Thomas Ranner +2
3D shape reconstruction typically requires identifying object features or textures in multiple images of a subject. This approach is not viable when the subject is semi-transparent…
Self-supervised 3D Human Pose Estimation from a Single Image
Jose Sosa, David Hogg
We propose a new self-supervised method for predicting 3D human body pose from a single image. The prediction network is trained from a dataset of unlabelled images depicting peopl…
Talking Head from Speech Audio using a Pre-trained Image Generator
Mohammed M. Alghamdi, He Wang, Andrew J. Bulpitt +1
We propose a novel method for generating high-resolution videos of talking-heads from speech audio and a single 'identity' image. Our method is based on a convolutional neural netw…
Anomaly detection using prediction error with Spatio-Temporal Convolutional LSTM
Hanh Thi Minh Tran, David Hogg
In this paper, we propose a novel method for video anomaly detection motivated by an existing architecture for sequence-to-sequence prediction and reconstruction using a spatio-tem…