no code implementations • 14 Sep 2023 • Iya Chivileva, Philip Lynch, Tomas E. Ward, Alan F. Smeaton
The contribution is an assessment of commonly used quality metrics, and a comparison of their performances and the performance of human evaluations on an open dataset of T2V videos.
no code implementations • 26 Apr 2020 • Qi She, Fan Feng, Qi Liu, Rosa H. M. Chan, Xinyue Hao, Chuanlin Lan, Qihan Yang, Vincenzo Lomonaco, German I. Parisi, Heechul Bae, Eoin Brophy, Baoquan Chen, Gabriele Graffieti, Vidit Goel, Hyonyoung Han, Sathursan Kanagarajah, Somesh Kumar, Siew-Kei Lam, Tin Lun Lam, Liang Ma, Davide Maltoni, Lorenzo Pellegrini, Duvindu Piyasena, ShiLiang Pu, Debdoot Sheet, Soonyong Song, Youngsung Son, Zhengwei Wang, Tomas E. Ward, Jianwen Wu, Meiqing Wu, Di Xie, Yangsheng Xu, Lin Yang, Qiaoyong Zhong, Liguang Zhou
This report summarizes IROS 2019-Lifelong Robotic Vision Competition (Lifelong Object Recognition Challenge) with methods and results from the top $8$ finalists (out of over~$150$ teams).
no code implementations • 2 Apr 2020 • Yingqi Gu, Akshay Zalkikar, Lara Kelly, Kieran Daly, Tomas E. Ward
Medication non-adherence is a widespread problem affecting over 50% of people who have chronic illness and need chronic treatment.
no code implementations • 30 Mar 2020 • Eoin Brophy, Willie Muehlhausen, Alan F. Smeaton, Tomas E. Ward
These same sampling frequencies also yielded a robust heart rate estimation which was comparative with that achieved at the more energy-intensive rate of 256 Hz.
1 code implementation • 5 Mar 2020 • Zhengwei Wang, Qi She, Alan F. Smeaton, Tomas E. Ward, Graham Healy
In this work, we introduce an evaluation metric called Neuroscore, for evaluating the performance of GANs, that more directly reflects psychoperceptual image quality through the utilization of brain signals.
3 code implementations • 19 Sep 2019 • Anne Marie Delaney, Eoin Brophy, Tomas E. Ward
Finally, we discuss the privacy concerns associated with sharing synthetic data produced by GANs and test their ability to withstand a simple membership inference attack.
3 code implementations • 4 Jun 2019 • Zhengwei Wang, Qi She, Tomas E. Ward
While several reviews for GANs have been presented to date, none have considered the status of this field based on their progress towards addressing practical challenges relevant to computer vision.
no code implementations • 28 May 2019 • Zhengwei Wang, Qi She, Eoin Brophy, Alan F. Smeaton, Tomas E. Ward, Graham Healy
Deep neural networks (DNNs) are inspired from the human brain and the interconnection between the two has been widely studied in the literature.
1 code implementation • 10 May 2019 • Zhengwei Wang, Qi She, Alan F. Smeaton, Tomas E. Ward, Graham Healy
In this work, we describe an evaluation metric we call Neuroscore, for evaluating the performance of GANs, that more directly reflects psychoperceptual image quality through the utilization of brain signals.
no code implementations • 14 Feb 2019 • Eoin Brophy, Zhengwei Wang, Tomas E. Ward
In the recent years Generative Adversarial Networks (GANs) have demonstrated significant progress in generating authentic looking data.
no code implementations • 15 Jan 2019 • Zhengwei Wang, Graham Healy, Alan F. Smeaton, Tomas E. Ward
In this paper we make two primary contributions to that field: 1) We propose a novel spatial filtering method which we call the Multiple Time Window LDA Beamformer (MTWLB) method; 2) we provide a comprehensive comparison of nine spatial filtering pipelines using three spatial filtering schemes namely, MTWLB, xDAWN, Common Spatial Pattern (CSP) and three linear classification methods Linear Discriminant Analysis (LDA), Bayesian Linear Regression (BLR) and Logistic Regression (LR).
no code implementations • 3 Dec 2018 • Eoin Brophy, José Juan Dominguez Veiga, Zhengwei Wang, Alan F. Smeaton, Tomas E. Ward
We then use the 2048 dimensional features from the penultimate layer as input to a support vector machine.
no code implementations • 10 Nov 2018 • Zhengwei Wang, Graham Healy, Alan F. Smeaton, Tomas E. Ward
We propose a novel approach that combines a brain-computer interface (BCI) with GANs to generate a measure we call Neuroscore, which closely mirrors the behavioral ground truth measured from participants tasked with discerning real from synthetic images.