794 citations · 2.7k across the 93 of their papers we have counts for
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QuickQual: Lightweight, convenient retinal image quality scoring with off-the-shelf pretrained models
Justin Engelmann, Amos Storkey, Miguel O. Bernabeu
Image quality remains a key problem for both traditional and deep learning (DL)-based approaches to retinal image analysis, but identifying poor quality images can be time consumin…
Prediction-Guided Distillation for Dense Object Detection
Chenhongyi Yang, Mateusz Ochal, Amos Storkey +1
Real-world object detection models should be cheap and accurate. Knowledge distillation (KD) can boost the accuracy of a small, cheap detection model by leveraging useful informati…
Global explainability in aligned image modalities
Justin Engelmann, Amos Storkey, Miguel O. Bernabeu
Deep learning (DL) models are very effective on many computer vision problems and increasingly used in critical applications. They are also inherently black box. A number of method…
Hamiltonian latent operators for content and motion disentanglement in image sequences
Asif Khan, Amos Storkey
We introduce \textit{HALO} -- a deep generative model utilising HAmiltonian Latent Operators to reliably disentangle content and motion information in image sequences. The \textit{…
Defining Benchmarks for Continual Few-Shot Learning
Antreas Antoniou, Massimiliano Patacchiola, Mateusz Ochal +1
Both few-shot and continual learning have seen substantial progress in the last years due to the introduction of proper benchmarks. That being said, the field has still to frame a…
DHOG: Deep Hierarchical Object Grouping
Luke Nicholas Darlow, Amos Storkey
Recently, a number of competitive methods have tackled unsupervised representation learning by maximising the mutual information between the representations produced from augmentat…