7 citations · 9 across the 4 of their papers we have counts for
4 papers
RobIn: A Robust Interpretable Deep Network for Schizophrenia Diagnosis
Daniel Organisciak, Hubert P. H. Shum, Ephraim Nwoye +1
Schizophrenia is a severe mental health condition that requires a long and complicated diagnostic process. However, early diagnosis is vital to control symptoms. Deep learning has…
Absolute Zero-Shot Learning
Rui Gao, Fan Wan, Daniel Organisciak +6
Considering the increasing concerns about data copyright and privacy issues, we present a novel Absolute Zero-Shot Learning (AZSL) paradigm, i.e., training a classifier with zero r…
UAV-ReID: A Benchmark on Unmanned Aerial Vehicle Re-identification in Video Imagery
Daniel Organisciak, Matthew Poyser, Aishah Alsehaim +4
As unmanned aerial vehicles (UAVs) become more accessible with a growing range of applications, the potential risk of UAV disruption increases. Recent development in deep learning…
Unmanned Aerial Vehicle Visual Detection and Tracking using Deep Neural Networks: A Performance Benchmark
Brian K. S. Isaac-Medina, Matt Poyser, Daniel Organisciak +3
Unmanned Aerial Vehicles (UAV) can pose a major risk for aviation safety, due to both negligent and malicious use. For this reason, the automated detection and tracking of UAV is a…