Search Results for author: Graham Healy

Found 20 papers, 2 papers with code

Memories in the Making: Predicting Video Memorability with Encoding Phase EEG

no code implementations16 Aug 2023 Lorin Sweeney, Graham Healy, Alan F. Smeaton

In a world of ephemeral moments, our brain diligently sieves through a cascade of experiences, like a skilled gold prospector searching for precious nuggets amidst the river's relentless flow.

EEG

Diffusing Surrogate Dreams of Video Scenes to Predict Video Memorability

no code implementations19 Dec 2022 Lorin Sweeney, Graham Healy, Alan F. Smeaton

As part of the MediaEval 2022 Predicting Video Memorability task we explore the relationship between visual memorability, the visual representation that characterises it, and the underlying concept portrayed by that visual representation.

Experiences from the MediaEval Predicting Media Memorability Task

no code implementations7 Dec 2022 Alba García Deco de Herrera, Mihai Gabriel Constantin, Chaire-Hélène Demarty, Camilo Fosco, Sebastian Halder, Graham Healy, Bogdan Ionescu, Ana Matran-Fernandez, Alan F. Smeaton, Mushfika Sultana, Lorin Sweeney

The Predicting Media Memorability task in the MediaEval evaluation campaign has been running annually since 2018 and several different tasks and data sets have been used in this time.

An Improved Subject-Independent Stress Detection Model Applied to Consumer-grade Wearable Devices

no code implementations18 Mar 2022 Van-Tu Ninh, Manh-Duy Nguyen, Sinéad Smyth, Minh-Triet Tran, Graham Healy, Binh T. Nguyen, Cathal Gurrin

Using our proposed model architecture, we compare the accuracy between stress detection models that use measures from each individual signal source, and one model employing the fusion of multiple sensor sources.

Management

Predicting Media Memorability: Comparing Visual, Textual and Auditory Features

no code implementations15 Dec 2021 Lorin Sweeney, Graham Healy, Alan F. Smeaton

This paper describes our approach to the Predicting Media Memorability task in MediaEval 2021, which aims to address the question of media memorability by setting the task of automatically predicting video memorability.

Overview of the EEG Pilot Subtask at MediaEval 2021: Predicting Media Memorability

no code implementations15 Dec 2021 Lorin Sweeney, Ana Matran-Fernandez, Sebastian Halder, Alba G. Seco de Herrera, Alan Smeaton, Graham Healy

The aim of the Memorability-EEG pilot subtask at MediaEval'2021 is to promote interest in the use of neural signals -- either alone or in combination with other data sources -- in the context of predicting video memorability by highlighting the utility of EEG data.

EEG Electroencephalogram (EEG)

Overview of The MediaEval 2021 Predicting Media Memorability Task

no code implementations11 Dec 2021 Rukiye Savran Kiziltepe, Mihai Gabriel Constantin, Claire-Helene Demarty, Graham Healy, Camilo Fosco, Alba Garcia Seco de Herrera, Sebastian Halder, Bogdan Ionescu, Ana Matran-Fernandez, Alan F. Smeaton, Lorin Sweeney

This paper describes the MediaEval 2021 Predicting Media Memorability}task, which is in its 4th edition this year, as the prediction of short-term and long-term video memorability remains a challenging task.

EEG Electroencephalogram (EEG)

The Influence of Audio on Video Memorability with an Audio Gestalt Regulated Video Memorability System

no code implementations23 Apr 2021 Lorin Sweeney, Graham Healy, Alan F. Smeaton

We introduce a novel multimodal deep learning-based late-fusion system that uses audio gestalt to estimate the influence of a given video's audio on its overall short-term recognition memorability, and selectively leverages audio features to make a prediction accordingly.

Multimodal Deep Learning Video Recognition

Leveraging Audio Gestalt to Predict Media Memorability

no code implementations31 Dec 2020 Lorin Sweeney, Graham Healy, Alan F. Smeaton

Memorability determines what evanesces into emptiness, and what worms its way into the deepest furrows of our minds.

Multimodal Deep Learning

Investigating Memorability of Dynamic Media

no code implementations31 Dec 2020 Phuc H. Le-Khac, Ayush K. Rai, Graham Healy, Alan F. Smeaton, Noel E. O'Connor

The Predicting Media Memorability task in MediaEval'20 has some challenging aspects compared to previous years.

Contrastive Representation Learning: A Framework and Review

no code implementations10 Oct 2020 Phuc H. Le-Khac, Graham Healy, Alan F. Smeaton

Examples of how contrastive learning has been applied in computer vision, natural language processing, audio processing, and others, as well as in Reinforcement Learning are also presented.

BIG-bench Machine Learning Contrastive Learning +2

A Neuro-AI Interface for Evaluating Generative Adversarial Networks

1 code implementation5 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.

Speech Synthesis

A Neuro-AI Interface: Learning DNNs from the Human Brain

no code implementations28 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.

Object Recognition Open-Ended Question Answering

Synthetic-Neuroscore: Using A Neuro-AI Interface for Evaluating Generative Adversarial Networks

1 code implementation10 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.

Image Generation Speech Synthesis

Spatial Filtering Pipeline Evaluation of Cortically Coupled Computer Vision System for Rapid Serial Visual Presentation

no code implementations15 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).

EEG Electroencephalogram (EEG) +3

Use of Neural Signals to Evaluate the Quality of Generative Adversarial Network Performance in Facial Image Generation

no code implementations10 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.

Brain Computer Interface Generative Adversarial Network +1

Exploring EEG for Object Detection and Retrieval

no code implementations9 Apr 2015 Eva Mohedano, Amaia Salvador, Sergi Porta, Xavier Giró-i-Nieto, Graham Healy, Kevin McGuinness, Noel O'Connor, Alan F. Smeaton

We show that it is indeed possible to detect such objects in complex images and, also, that users with previous knowledge on the dataset or experience with the RSVP outperform others.

Content-Based Image Retrieval EEG +5

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