Outputs (474)

On the Impact of Using X-Ray Energy Response Imagery for Object Detection via Convolutional Neural Networks (2021)
Presentation / Conference Contribution

Automatic detection of prohibited items within complex and cluttered X-ray security imagery is essential to maintaining transport security, where prior work on automatic prohibited item detection focus primarily on pseudo-colour (rgb) X-ray imagery.... Read More about On the Impact of Using X-Ray Energy Response Imagery for Object Detection via Convolutional Neural Networks.

Interpreting Deep Learning based Cerebral Palsy Prediction with Channel Attention (2021)
Presentation / Conference Contribution

Early prediction of cerebral palsy is essential as it leads to early treatment and monitoring. Deep learning has shown promising results in biomedical engineering thanks to its capacity of modelling complicated data with its non-linear architecture.... Read More about Interpreting Deep Learning based Cerebral Palsy Prediction with Channel Attention.

Makeup Style Transfer on Low-quality Images with Weighted Multi-scale Attention (2021)
Presentation / Conference Contribution

Facial makeup style transfer is an extremely challenging sub-field of image-to-image-translation. Due to this difficulty, state-of-the-art results are mostly reliant on the Face Parsing Algorithm, which segments a face into parts in order to easily e... Read More about Makeup Style Transfer on Low-quality Images with Weighted Multi-scale Attention.

Competitive Simplicity for Multi-Task Learning for Real-Time Foggy Scene Understanding via Domain Adaptation (2021)
Presentation / Conference Contribution

— Automotive scene understanding under adverse weather conditions raises a realistic and challenging problem attributable to poor outdoor scene visibility (e.g. foggy weather). However, because most contemporary scene understanding approaches are app... Read More about Competitive Simplicity for Multi-Task Learning for Real-Time Foggy Scene Understanding via Domain Adaptation.

Data Augmentation via Mixed Class Interpolation using Cycle-Consistent Generative Adversarial Networks Applied to Cross-Domain Imagery (2021)
Presentation / Conference Contribution

Machine learning driven object detection and classification within non-visible imagery has an important role in many fields such as night vision, all-weather surveillance and aviation security. However, such applications often suffer due to the limit... Read More about Data Augmentation via Mixed Class Interpolation using Cycle-Consistent Generative Adversarial Networks Applied to Cross-Domain Imagery.

Stable Hand Pose Estimation under Tremor via Graph Neural Network (2021)
Presentation / Conference Contribution

Hand pose estimation, which predicts the spatial location of hand joints, is a fundamental task in VR/AR applications. Although existing methods can recover hand pose competently, the tremor issue occurring in hand motion has not been completely solv... Read More about Stable Hand Pose Estimation under Tremor via Graph Neural Network.

Measuring Hidden Bias within Face Recognition via Racial Phenotypes (2022)
Presentation / Conference Contribution

Recent work reports disparate performance for intersectional racial groups across face recognition tasks: face verification and identification. However, the definition of those racial groups has a significant impact on the underlying findings of such... Read More about Measuring Hidden Bias within Face Recognition via Racial Phenotypes.

Operationalizing Convolutional Neural Network Architectures for Prohibited Object Detection in X-Ray Imagery (2021)
Presentation / Conference Contribution

Leveraging Synthetic Subject Invariant EEG Signals for Zero Calibration BCI (2021)
Presentation / Conference Contribution

Recently, substantial progress has been made in the area of Brain-Computer Interface (BCI) using modern machine learning techniques to decode and interpret brain signals. While Electroencephalography (EEG) has provided a non-invasive method of interf... Read More about Leveraging Synthetic Subject Invariant EEG Signals for Zero Calibration BCI.

Multi-Class 3D Object Detection Within Volumetric 3D Computed Tomography Baggage Security Screening Imagery (2021)
Presentation / Conference Contribution

Automatic detection of prohibited objects within passenger baggage is important for aviation security. X-ray Computed Tomography (CT) based 3D imaging is widely used in airports for aviation security screening whilst prior work on automatic prohibite... Read More about Multi-Class 3D Object Detection Within Volumetric 3D Computed Tomography Baggage Security Screening Imagery.

Improving Robotic Grasping on Monocular Images Via Multi-Task Learning and Positional Loss (2021)
Presentation / Conference Contribution

In this paper we introduce two methods of improving real-time object grasping performance from monocular colour images in an end-to-end CNN architecture. The first is the addition of an auxiliary task during model training (multi-task learning). Our... Read More about Improving Robotic Grasping on Monocular Images Via Multi-Task Learning and Positional Loss.

Attack-agnostic Adversarial Detection on Medical Data Using Explainable Machine Learning (2021)
Presentation / Conference Contribution

Explainable machine learning has become increasingly prevalent, especially in healthcare where explainable models are vital for ethical and trusted automated decision making. Work on the susceptibility of deep learning models to adversarial attacks h... Read More about Attack-agnostic Adversarial Detection on Medical Data Using Explainable Machine Learning.

On Modality Bias in the TVQA Dataset (2020)
Presentation / Conference Contribution

TVQA is a large scale video question answering (video-QA) dataset based on popular TV shows. The questions were specifically designed to require “both vision and language understanding to answer”. In this work, we demonstrate an inherent bias in the... Read More about On Modality Bias in the TVQA Dataset.

On the Impact of Lossy Image and Video Compression on the Performance of Deep Convolutional Neural Network Architectures (2021)
Presentation / Conference Contribution

Recent advances in generalized image understanding have seen a surge in the use of deep convolutional neural networks (CNN) across a broad range of image-based detection, classification and prediction tasks. Whilst the reported performance of these a... Read More about On the Impact of Lossy Image and Video Compression on the Performance of Deep Convolutional Neural Network Architectures.

STGAE: Spatial-Temporal Graph Auto-Encoder for Hand Motion Denoising (2021)
Presentation / Conference Contribution

Hand object interaction in mixed reality (MR) relies on the accurate tracking and estimation of human hands, which provide users with a sense of immersion. However, raw captured hand motion data always contains errors such as joints occlusion, disloc... Read More about STGAE: Spatial-Temporal Graph Auto-Encoder for Hand Motion Denoising.

Semantics-STGCNN: A Semantics-guided Spatial-Temporal Graph Convolutional Network for Multi-class Trajectory Prediction (2021)
Presentation / Conference Contribution

Predicting the movement trajectories of multiple classes of road users in real-world scenarios is a challenging task due to the diverse trajectory patterns. While recent works of pedestrian trajectory prediction successfully modelled the influence of... Read More about Semantics-STGCNN: A Semantics-guided Spatial-Temporal Graph Convolutional Network for Multi-class Trajectory Prediction.

Exploring Racial Bias within Face Recognition via per-subject Adversarially-Enabled Data Augmentation (2020)
Presentation / Conference Contribution

A Generative Bayesian Graph Attention Network for Semi-supervised Classification on Scarce Data (2021)
Presentation / Conference Contribution

This research focuses on semi-supervised classification tasks, specifically for graph-structured data under datascarce situations. It is known that the performance of conventional supervised graph convolutional models is mediocre at classification ta... Read More about A Generative Bayesian Graph Attention Network for Semi-supervised Classification on Scarce Data.

Source Class Selection with Label Propagation for Partial Domain Adaptation (2021)
Presentation / Conference Contribution

In traditional unsupervised domain adaptation problems, the target domain is assumed to share the same set of classes as the source domain. In practice, there exist situations where target-domain data are from only a subset of source-domain classes a... Read More about Source Class Selection with Label Propagation for Partial Domain Adaptation.

Unsupervised Domain Adaptation via Structured Prediction Based Selective Pseudo-Labeling (2020)
Presentation / Conference Contribution

Unsupervised domain adaptation aims to address the problem of classifying unlabeled samples from the target domain whilst labeled samples are only available from the source domain and the data distributions are different in these two domains. As a re... Read More about Unsupervised Domain Adaptation via Structured Prediction Based Selective Pseudo-Labeling.