108 citations · 839 across the 102 of their papers we have counts for
21 papers · 1 filter
Deep Learning for Medical Anomaly Detection -- A Survey
Tharindu Fernando, Harshala Gammulle, Simon Denman +2
Machine learning-based medical anomaly detection is an important problem that has been extensively studied. Numerous approaches have been proposed across various medical applicatio…
Patient-independent Epileptic Seizure Prediction using Deep Learning Models
Theekshana Dissanayake, Tharindu Fernando, Simon Denman +2
Objective: Epilepsy is one of the most prevalent neurological diseases among humans and can lead to severe brain injuries, strokes, and brain tumors. Early detection of seizures ca…
Domain Generalization in Biosignal Classification
Theekshana Dissanayake, Tharindu Fernando, Simon Denman +3
Objective: When training machine learning models, we often assume that the training data and evaluation data are sampled from the same distribution. However, this assumption is vio…
Fast & Slow Learning: Incorporating Synthetic Gradients in Neural Memory Controllers
Tharindu Fernando, Simon Denman, Sridha Sridharan +1
Neural Memory Networks (NMNs) have received increased attention in recent years compared to deep architectures that use a constrained memory. Despite their new appeal, the success…
Locus: LiDAR-based Place Recognition using Spatiotemporal Higher-Order Pooling
Kavisha Vidanapathirana, Peyman Moghadam, Ben Harwood +3
Place Recognition enables the estimation of a globally consistent map and trajectory by providing non-local constraints in Simultaneous Localisation and Mapping (SLAM). This paper…
Complex-valued Iris Recognition Network
Kien Nguyen, Clinton Fookes, Sridha Sridharan +1
In this work, we design a fully complex-valued neural network for the task of iris recognition. Unlike the problem of general object recognition, where real-valued neural networks…
Multi-modal Fusion for Single-Stage Continuous Gesture Recognition
Harshala Gammulle, Simon Denman, Sridha Sridharan +1
Gesture recognition is a much studied research area which has myriad real-world applications including robotics and human-machine interaction. Current gesture recognition methods h…
Attention Driven Fusion for Multi-Modal Emotion Recognition
Darshana Priyasad, Tharindu Fernando, Simon Denman +2
Deep learning has emerged as a powerful alternative to hand-crafted methods for emotion recognition on combined acoustic and text modalities. Baseline systems model emotion informa…
Elasticity Meets Continuous-Time: Map-Centric Dense 3D LiDAR SLAM
Chanoh Park, Peyman Moghadam, Jason Williams +3
Map-centric SLAM utilizes elasticity as a means of loop closure. This approach reduces the cost of loop closure while still provides large-scale fusion-based dense maps, when compa…
Two-Stream Deep Feature Modelling for Automated Video Endoscopy Data Analysis
Harshala Gammulle, Simon Denman, Sridha Sridharan +1
Automating the analysis of imagery of the Gastrointestinal (GI) tract captured during endoscopy procedures has substantial potential benefits for patients, as it can provide diagno…
Memory based fusion for multi-modal deep learning
Darshana Priyasad, Tharindu Fernando, Simon Denman +2
The use of multi-modal data for deep machine learning has shown promise when compared to uni-modal approaches with fusion of multi-modal features resulting in improved performance…
Meta Transfer Learning for Emotion Recognition
Dung Nguyen, Sridha Sridharan, Duc Thanh Nguyen +3
Deep learning has been widely adopted in automatic emotion recognition and has lead to significant progress in the field. However, due to insufficient annotated emotion datasets, p…
End-to-End Domain Adaptive Attention Network for Cross-Domain Person Re-Identification
Amena Khatun, Simon Denman, Sridha Sridharan +1
Person re-identification (re-ID) remains challenging in a real-world scenario, as it requires a trained network to generalise to totally unseen target data in the presence of varia…
Hierarchical Attention Network for Action Segmentation
Harshala Gammulle, Simon Denman, Sridha Sridharan +1
The temporal segmentation of events is an essential task and a precursor for the automatic recognition of human actions in the video. Several attempts have been made to capture fra…
A Robust Interpretable Deep Learning Classifier for Heart Anomaly Detection Without Segmentation
Theekshana Dissanayake, Tharindu Fernando, Simon Denman +3
Traditionally, abnormal heart sound classification is framed as a three-stage process. The first stage involves segmenting the phonocardiogram to detect fundamental heart sounds; a…
Deep Auto-Encoders with Sequential Learning for Multimodal Dimensional Emotion Recognition
Dung Nguyen, Duc Thanh Nguyen, Rui Zeng +5
Multimodal dimensional emotion recognition has drawn a great attention from the affective computing community and numerous schemes have been extensively investigated, making a sign…
Heart Sound Segmentation using Bidirectional LSTMs with Attention
Tharindu Fernando, Houman Ghaemmaghami, Simon Denman +3
This paper proposes a novel framework for the segmentation of phonocardiogram (PCG) signals into heart states, exploiting the temporal evolution of the PCG as well as considering t…
Temporarily-Aware Context Modelling using Generative Adversarial Networks for Speech Activity Detection
Tharindu Fernando, Sridha Sridharan, Mitchell McLaren +3
This paper presents a novel framework for Speech Activity Detection (SAD). Inspired by the recent success of multi-task learning approaches in the speech processing domain, we prop…
Joint Deep Cross-Domain Transfer Learning for Emotion Recognition
Dung Nguyen, Sridha Sridharan, Duc Thanh Nguyen +4
Deep learning has been applied to achieve significant progress in emotion recognition. Despite such substantial progress, existing approaches are still hindered by insufficient tra…
Learning Test-time Augmentation for Content-based Image Retrieval
Osman Tursun, Simon Denman, Sridha Sridharan +1
Off-the-shelf convolutional neural network features achieve outstanding results in many image retrieval tasks. However, their invariance to target data is pre-defined by the networ…
Spatiotemporal Camera-LiDAR Calibration: A Targetless and Structureless Approach
Chanoh Park, Peyman Moghadam, Soohwan Kim +2
The demand for multimodal sensing systems for robotics is growing due to the increase in robustness, reliability and accuracy offered by these systems. These systems also need to b…