Outputs (451)

Transforming Fake News: Robust Generalisable News Classification Using Transformers (2021)
Presentation / Conference Contribution

As online news has become increasingly popular and fake news increasingly prevalent, the ability to audit the veracity of online news content has become more important than ever. Such a task represents a binary classification challenge, for which tra... Read More about Transforming Fake News: Robust Generalisable News Classification Using Transformers.

A King’s Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation (2019)
Presentation / Conference Contribution

Quality perception and discrimination thresholds in quantised triangle meshes (2021)
Presentation / Conference Contribution

At certain stages of the graphics pipeline, and most notably during compression for transmission and storage, triangle meshes may undergo a fixed-point arithmetic quantisation of their vertex coordinates. This paper presents the results of a psychoph... Read More about Quality perception and discrimination thresholds in quantised triangle meshes.

MuLD: The Multitask Long Document Benchmark (2022)
Presentation / Conference Contribution

The impressive progress in NLP techniques has been driven by the development of multi-task benchmarks such as GLUE and SuperGLUE. While these benchmarks focus on tasks for one or two input sentences, there has been exciting work in designing efficien... Read More about MuLD: The Multitask Long Document Benchmark.

Re-ID-AR: Improved Person Re-identification in Video via Joint Weakly Supervised Action Recognition (2021)
Presentation / Conference Contribution

We uniquely consider the task of joint person re-identification (Re-ID) and action recognition in video as a multi-task problem. In addition to the broader potential of joint Re-ID and action recognition within the context of automated multi-camera s... Read More about Re-ID-AR: Improved Person Re-identification in Video via Joint Weakly Supervised Action Recognition.

Emulating Foveated Path Tracing (2021)
Presentation / Conference Contribution

At full resolution, path tracing cannot be deployed in real-time based on current graphics hardware due to slow convergence times and noisy outputs, despite recent advances in denoisers. In this work, we develop a perceptual sandbox emulating a fovea... Read More about Emulating Foveated Path Tracing.

Enhanced Methods for Evolution in-Materio Processors (2022)
Presentation / Conference Contribution

Evolution-in-Materio (EiM) is an unconventional computing paradigm, which uses an Evolutionary Algorithm (EA) to configure a material's parameters so that it can perform a computational task. While EiM processors show promise, slow manufacturing and... Read More about Enhanced Methods for Evolution in-Materio Processors.

Bi-projection-based Foreground-aware Omnidirectional Depth Prediction (2021)
Presentation / Conference Contribution

Due to the increasing availability of commercial 360- degree cameras, accurate depth prediction for omnidirectional images can be beneficial to a wide range of applications including video editing and augmented reality. Regarding existing methods, so... Read More about Bi-projection-based Foreground-aware Omnidirectional Depth Prediction.

Human-centric Autonomous Driving in an AV-Pedestrian Interactive Environment Using SVO (2021)
Presentation / Conference Contribution

As Autonomous Vehicles (AV) are becoming a reality, the design of efficient motion control algorithms will have to deal with the unpredictable and interactive nature of other road users. Current AV motion planning algorithms suffer from the freezing... Read More about Human-centric Autonomous Driving in an AV-Pedestrian Interactive Environment Using SVO.

Agree to Disagree: When Deep Learning Models With Identical Architectures Produce Distinct Explanations (2022)
Presentation / Conference Contribution

Deep Learning of neural networks has progressively become more prominent in healthcare with models reaching, or even surpassing, expert accuracy levels. However, these success stories are tainted by concerning reports on the lack of model transparenc... Read More about Agree to Disagree: When Deep Learning Models With Identical Architectures Produce Distinct Explanations.

Rank over Class: The Untapped Potential of Ranking in Natural Language Processing (2021)
Presentation / Conference Contribution

Text classification has long been a staple within Natural Language Processing (NLP) with applications spanning across diverse areas such as sentiment analysis, recommender systems and spam detection. With such a powerful solution, it is often temptin... Read More about Rank over Class: The Untapped Potential of Ranking in Natural Language Processing.

Identification of Driver Phone Usage Violations via State-of-the-Art Object Detection with Tracking (2021)
Presentation / Conference Contribution

The use of mobiles phones when driving has been a major factor when it comes to road traffic incidents and the process of capturing such violations can be a laborious task. Advancements in both modern object detection frameworks and high-performance... Read More about Identification of Driver Phone Usage Violations via State-of-the-Art Object Detection with Tracking.

Volenti non fit injuria: Ransomware and its Victims (2019)
Presentation / Conference Contribution

With the recent growth in the number of malicious activities on the internet, cybersecurity research has seen a boost in the past few years. However, as certain variants of malware can provide highly lucrative opportunities for bad actors, significan... Read More about Volenti non fit injuria: Ransomware and its Victims.

Contraband Materials Detection Within Volumetric 3D Computed Tomography Baggage Security Screening Imagery (2021)
Presentation / Conference Contribution

DurLAR: A High-Fidelity 128-Channel LiDAR Dataset with Panoramic Ambient and Reflectivity Imagery for Multi-Modal Autonomous Driving Applications (2021)
Presentation / Conference Contribution

We present DurLAR, a high-fidelity 128-channel 3D LiDAR dataset with panoramic ambient (near infrared) and reflectivity imagery, as well as a sample benchmark task using depth estimation for autonomous driving applications. Our driving platform is eq... Read More about DurLAR: A High-Fidelity 128-Channel LiDAR Dataset with Panoramic Ambient and Reflectivity Imagery for Multi-Modal Autonomous Driving Applications.

UAV-ReID: A Benchmark on Unmanned Aerial Vehicle Re-Identification in Video Imagery (2022)
Presentation / Conference Contribution

As unmanned aerial vehicles (UAV) become more accessible with a growing range of applications, the risk of UAV disruption increases. Recent development in deep learning allows vision-based counter-UAV systems to detect and track UAVs with a single ca... Read More about UAV-ReID: A Benchmark on Unmanned Aerial Vehicle Re-Identification in Video Imagery.

On the Evaluation of Semi-Supervised 2D Segmentation for Volumetric 3D Computed Tomography Baggage Security Screening (2021)
Presentation / Conference Contribution

We address the automatic contraband material detection problem within volumetric 3D Computed Tomography (CT) data for baggage security screening. Distinct from the prohibited item detection using object detection techniques, contraband material detec... Read More about On the Evaluation of Semi-Supervised 2D Segmentation for Volumetric 3D Computed Tomography Baggage Security Screening.

Continuous Multi-modal Emotion Prediction in Video based on Recurrent Neural Network Variants with Attention (2021)
Presentation / Conference Contribution

Unmanned Aerial Vehicle Visual Detection and Tracking using Deep Neural Networks: A Performance Benchmark (2021)
Presentation / Conference Contribution

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 fundamental task in aerial security systems. Common technologies for... Read More about Unmanned Aerial Vehicle Visual Detection and Tracking using Deep Neural Networks: A Performance Benchmark.

Towards Equal Gender Representation in the Annotations of Toxic Language Detection (2021)
Presentation / Conference Contribution

Classifiers tend to propagate biases present in the data on which they are trained. Hence, it is important to understand how the demographic identities of the annotators of comments affect the fairness of the resulting model. In this paper, we focus... Read More about Towards Equal Gender Representation in the Annotations of Toxic Language Detection.