Search Results for author: Alan F. Smeaton

Found 38 papers, 9 papers with code

Analysis of Individual Conversational Volatility in Tandem Telecollaboration for Second Language Learning

no code implementations28 Jun 2022 Alan F. Smeaton, Aparajita Dey-Plissonneau, Hyowon Lee, Mingming Liu, Michael Scriney

Second language learning can be enabled by tandem collaboration where students are grouped into video conference calls while learning the native language of other student(s) on the calls.

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 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.


Using a GAN to Generate Adversarial Examples to Facial Image Recognition

no code implementations30 Nov 2021 Andrew Merrigan, Alan F. Smeaton

Images posted online present a privacy concern in that they may be used as reference examples for a facial recognition system.

Face Recognition Knowledge Distillation

Image Segmentation to Identify Safe Landing Zones for Unmanned Aerial Vehicles

no code implementations29 Nov 2021 Joe Kinahan, Alan F. Smeaton

There is a marked increase in delivery services in urban areas, and with Jeff Bezos claiming that 86% of the orders that Amazon ships weigh less than 5 lbs, the time is ripe for investigation into economical methods of automating the final stage of the delivery process.

Semantic Segmentation

Facilitating reflection in teletandem through automatically generated conversation metrics and playback video

no code implementations16 Nov 2021 Aparajita Dey-Plissonneau, Hyowon Lee, Michael Scriney, Alan F. Smeaton, Vincent Pradier, Hamza Riaz

This pilot study focuses on a tool called L2L that allows second language (L2) learners to visualise and analyse their Zoom interactions with native speakers.

Computer Vision for Supporting Image Search

no code implementations16 Nov 2021 Alan F. Smeaton

Computer vision and multimedia information processing have made extreme progress within the last decade and many tasks can be done with a level of accuracy as if done by humans, or better.

Computer Vision Image Retrieval

Improved CNN-based Learning of Interpolation Filters for Low-Complexity Inter Prediction in Video Coding

1 code implementation16 Jun 2021 Luka Murn, Saverio Blasi, Alan F. Smeaton, Marta Mrak

The approach requires a single neural network to be trained from which a full quarter-pixel interpolation filter set is derived, as the network is easily interpretable due to its linear structure.

Explainable Models Motion Compensation +1

Translation Quality Assessment: A Brief Survey on Manual and Automatic Methods

1 code implementation MoTra (NoDaLiDa) 2021 Lifeng Han, Gareth J. F. Jones, Alan F. Smeaton

To facilitate effective translation modeling and translation studies, one of the crucial questions to address is how to assess translation quality.

Machine Translation Natural Language Processing +4

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

Chinese Character Decomposition for Neural MT with Multi-Word Expressions

1 code implementation NoDaLiDa 2021 Lifeng Han, Gareth J. F. Jones, Alan F. Smeaton, Paolo Bolzoni

To investigate the impact of Chinese decomposition embedding in detail, i. e., radical, stroke, and intermediate levels, and how well these decompositions represent the meaning of the original character sequences, we carry out analysis with both automated and human evaluation of MT.

Machine Translation Translation

Attention-Based Neural Networks for Chroma Intra Prediction in Video Coding

no code implementations9 Feb 2021 Marc Górriz, Saverio Blasi, Alan F. Smeaton, Noel E. O'Connor, Marta Mrak

Simplifications include a framework for reducing the overhead of the convolutional operations, a simplified cross-component processing model integrated into the original architecture, and a methodology to perform integer-precision approximations with the aim to obtain fast and hardware-aware implementations.

Detecting Deepfake Videos Using Euler Video Magnification

no code implementations27 Jan 2021 Rashmiranjan Das, Gaurav Negi, Alan F. Smeaton

This involves replacing the face of an individual from a source video with the face of a second person, in the destination video.

Face Swapping

Attention Based Video Summaries of Live Online Zoom Classes

no code implementations15 Jan 2021 Hyowon Lee, Mingming Liu, Hamza Riaz, Navaneethan Rajasekaren, Michael Scriney, Alan F. Smeaton

We can also factor in other criteria into video summary generation such as parts where the student was not paying attention while others in the class were, and parts of the video that other students have replayed extensively which a given student has not.

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.

Using GANs to Synthesise Minimum Training Data for Deepfake Generation

no code implementations10 Nov 2020 Simranjeet Singh, Rajneesh Sharma, Alan F. Smeaton

There are many applications of Generative Adversarial Networks (GANs) in fields like computer vision, natural language processing, speech synthesis, and more.

Computer Vision Face Swapping +3

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.

Computer Vision Contrastive Learning +3

A Comparative Study of Existing and New Deep Learning Methods for Detecting Knee Injuries using the MRNet Dataset

1 code implementation5 Oct 2020 David Azcona, Kevin McGuinness, Alan F. Smeaton

Overall we achieved a performance of 93. 4% AUC on the validation data by using the more recent deep learning architectures and data augmentation strategies.

Data Augmentation Transfer Learning

TRECVID 2019: An Evaluation Campaign to Benchmark Video Activity Detection, Video Captioning and Matching, and Video Search & Retrieval

no code implementations21 Sep 2020 George Awad, Asad A. Butt, Keith Curtis, Yooyoung Lee, Jonathan Fiscus, Afzal Godil, Andrew Delgado, Jesse Zhang, Eliot Godard, Lukas Diduch, Alan F. Smeaton, Yvette Graham, Wessel Kraaij, Georges Quenot

The TREC Video Retrieval Evaluation (TRECVID) 2019 was a TREC-style video analysis and retrieval evaluation, the goal of which remains to promote progress in research and development of content-based exploitation and retrieval of information from digital video via open, metrics-based evaluation.

Action Detection Activity Detection +5

Tracking Skin Colour and Wrinkle Changes During Cosmetic Product Trials Using Smartphone Images

no code implementations4 Aug 2020 Alan F. Smeaton, Swathikiran Srungavarapu, Cyril Messaraa, Claire Tansey

Materials and Methods: 12 women aged 30 to 60 years participated in a product trial and had close-up images of the cheek and temple regions of their faces taken with a high-resolution Antera 3D CS camera at the start and end of a 4-week period.

Utilising Visual Attention Cues for Vehicle Detection and Tracking

no code implementations31 Jul 2020 Feiyan Hu, Venkatesh G M, Noel E. O'Connor, Alan F. Smeaton, Suzanne Little

We investigate: 1) How a visual attention map such as a \emph{subjectness} attention or saliency map and an \emph{objectness} attention map can facilitate region proposal generation in a 2-stage object detector; 2) How a visual attention map can be used for tracking multiple objects.

object-detection Object Detection +1

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.

Computer Vision Natural Language Processing +1

End-to-End Conditional GAN-based Architectures for Image Colourisation

1 code implementation26 Aug 2019 Marc Górriz, Marta Mrak, Alan F. Smeaton, Noel E. O'Connor

In this work recent advances in conditional adversarial networks are investigated to develop an end-to-end architecture based on Convolutional Neural Networks (CNNs) to directly map realistic colours to an input greyscale image.

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

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.

Computer Vision Image Generation +2

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).

Computer Vision EEG +2

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.

Image Generation

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 +2

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