Search Results for author:
Found 68 papers, 7 papers with code
Automatic Radar Signal Detection and FFT Estimation using Deep Learning
This paper addresses a critical preliminary step in radar signal processing: detecting the presence of a radar signal and robustly estimating its bandwidth.
Deep Learning Approaches for Seizure Video Analysis: A Review
Historically, these approaches have been used for disease detection, classification, and prediction using diagnostic data; however, there has been limited exploration of their application in evaluating video-based motion detection in the clinical epileptology setting.
Multi-stage Learning for Radar Pulse Activity Segmentation
Radio signal recognition is a crucial function in electronic warfare.
Multi-task Learning for Radar Signal Characterisation
Radio signal recognition is a crucial task in both civilian and military applications, as accurate and timely identification of unknown signals is an essential part of spectrum management and electronic warfare.
Remembering What Is Important: A Factorised Multi-Head Retrieval and Auxiliary Memory Stabilisation Scheme for Human Motion Prediction
Humans exhibit complex motions that vary depending on the task that they are performing, the interactions they engage in, as well as subject-specific preferences.
Towards Self-Explainability of Deep Neural Networks with Heatmap Captioning and Large-Language Models
We proposed a template-based image captioning approach for context modelling to create text-based contextual information from the heatmap and input data.
Explainable Artificial Intelligence (XAI)
Image Captioning
+2
Automated Coronary Arteries Labeling Via Geometric Deep Learning
Automatic labelling of anatomical structures, such as coronary arteries, is critical for diagnosis, yet existing (non-deep learning) methods are limited by a reliance on prior topological knowledge of the expected tree-like structures.
Vision-Based Activity Recognition in Children with Autism-Related Behaviors
Advances in machine learning and contactless sensors have enabled the understanding complex human behaviors in a healthcare setting.
SESS: Saliency Enhancing with Scaling and Sliding
High-quality saliency maps are essential in several machine learning application areas including explainable AI and weakly supervised object detection and segmentation.
Towards On-Board Panoptic Segmentation of Multispectral Satellite Images
To address this challenge, we propose a multimodal teacher network based on a cross-modality attention-based fusion strategy to improve the segmentation accuracy by exploiting data from multiple modes.
Continuous Human Action Recognition for Human-Machine Interaction: A Review
With advances in data-driven machine learning research, a wide variety of prediction models have been proposed to capture spatio-temporal features for the analysis of video streams.
Privacy-Preserving In-Bed Pose Monitoring: A Fusion and Reconstruction Study
In this paper we explore the effective use of images from multiple non-visual and privacy-preserving modalities such as depth, long-wave infrared (LWIR) and pressure maps for the task of in-bed pose estimation in two settings.
In-Bed Human Pose Estimation from Unseen and Privacy-Preserving Image Domains
Medical applications have benefited greatly from the rapid advancement in computer vision.
Multi-Slice Net: A novel light weight framework for COVID-19 Diagnosis
This paper presents a novel lightweight COVID-19 diagnosis framework using CT scans.
A Survey on Graph-Based Deep Learning for Computational Histopathology
With the remarkable success of representation learning for prediction problems, we have witnessed a rapid expansion of the use of machine learning and deep learning for the analysis of digital pathology and biopsy image patches.
Robust and Interpretable Temporal Convolution Network for Event Detection in Lung Sound Recordings
We exceed the state-of-the-art results in all evaluations.
Video-Based Inpatient Fall Risk Assessment: A Case Study
Inpatient falls are a serious safety issue in hospitals and healthcare facilities.
Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future
It has become critical to explore how machine learning and specifically deep learning methods can be exploited to analyse healthcare data.
Towards Interpretable Attention Networks for Cervical Cancer Analysis
Here, we evaluate various state-of-the-art deep learning models and attention-based frameworks for the classification of images of multiple cervical cells.
Semantic Consistency and Identity Mapping Multi-Component Generative Adversarial Network for Person Re-Identification
In a real world environment, person re-identification (Re-ID) is a challenging task due to variations in lighting conditions, viewing angles, pose and occlusions.
Pose-driven Attention-guided Image Generation for Person Re-Identification
Person re-identification (re-ID) concerns the matching of subject images across different camera views in a multi camera surveillance system.
Learning Regional Attention over Multi-resolution Deep Convolutional Features for Trademark Retrieval
However, R-MAC suffers in the presence of background clutter/trivial regions and scale variance, and discards important spatial information.
An Efficient Framework for Zero-Shot Sketch-Based Image Retrieval
Recently, Zero-shot Sketch-based Image Retrieval (ZS-SBIR) has attracted the attention of the computer vision community due to it's real-world applications, and the more realistic and challenging setting than found in SBIR.
Deep Learning for Medical Anomaly Detection -- A Survey
Machine learning-based medical anomaly detection is an important problem that has been extensively studied.
Sparse Convolutions on Continuous Domains for Point Cloud and Event Stream Networks
Image convolutions have been a cornerstone of a great number of deep learning advances in computer vision.
Patient-independent Epileptic Seizure Prediction using Deep Learning Models
Patient-independent seizure prediction models are designed to offer accurate performance across multiple subjects within a dataset, and have been identified as a real-world solution to the seizure prediction problem.
Domain Generalization in Biosignal Classification
Conclusion: Recognizing the complexity induced by the inherent temporal nature of biosignal data, the two-stage method proposed in this study is able to effectively simplify the whole process of domain generalization while demonstrating good results on unseen domains and the adopted basis domains.
Fast & Slow Learning: Incorporating Synthetic Gradients in Neural Memory Controllers
In addition, we demonstrate the practical implications of the proposed learning strategy, where the feedback path can be shared among multiple neural memory networks as a mechanism for knowledge sharing.
Multi-modal Fusion for Single-Stage Continuous Gesture Recognition
Gesture recognition is a much studied research area which has myriad real-world applications including robotics and human-machine interaction.
Attention Driven Fusion for Multi-Modal Emotion Recognition
In this paper, we present a deep learning-based approach to exploit and fuse text and acoustic data for emotion classification.
Memory based fusion for multi-modal deep learning
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 in several applications.
Two-Stream Deep Feature Modelling for Automated Video Endoscopy Data Analysis
Automating the analysis of imagery of the Gastrointestinal (GI) tract captured during endoscopy procedures has substantial potential benefits for patients, as it can provide diagnostic support to medical practitioners and reduce mistakes via human error.
Meta Transfer Learning for Emotion Recognition
To mitigate this challenge, transfer learning performing fine-tuning on pre-trained models has been applied.
Facial Expression Recognition
Facial Expression Recognition (FER)
+2
A Robust Interpretable Deep Learning Classifier for Heart Anomaly Detection Without Segmentation
In this study, we explicitly examine the importance of heart sound segmentation as a prior step for heart sound classification, and then seek to apply the obtained insights to propose a robust classifier for abnormal heart sound detection.
Hierarchical Attention Network for Action Segmentation
The temporal segmentation of events is an essential task and a precursor for the automatic recognition of human actions in the video.
End-to-End Domain Adaptive Attention Network for Cross-Domain Person Re-Identification
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 variations across domains.
Heart Sound Segmentation using Bidirectional LSTMs with Attention
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 the salient information that it provides for the detection of the heart state.
Temporarily-Aware Context Modelling using Generative Adversarial Networks for Speech Activity Detection
This paper presents a novel framework for Speech Activity Detection (SAD).
Joint Deep Cross-Domain Transfer Learning for Emotion Recognition
Deep learning has been applied to achieve significant progress in emotion recognition.
Learning Test-time Augmentation for Content-based Image Retrieval
However, their invariance to target data is pre-defined by the network architecture and training data.
Predicting the Future: A Jointly Learnt Model for Action Anticipation
Inspired by human neurological structures for action anticipation, we present an action anticipation model that enables the prediction of plausible future actions by forecasting both the visual and temporal future.
MTRNet++: One-stage Mask-based Scene Text Eraser
The results of ablation studies demonstrate that the proposed multi-branch architecture with attention blocks is effective and essential.
Neural Memory Networks for Seizure Type Classification
Inspired by recent advances in neural memory networks (NMNs), we introduce a novel approach for the classification of seizure type using electrophysiological data.
Exploiting Human Social Cognition for the Detection of Fake and Fraudulent Faces via Memory Networks
Advances in computer vision have brought us to the point where we have the ability to synthesise realistic fake content.
Neural Memory Plasticity for Anomaly Detection
In the domain of machine learning, Neural Memory Networks (NMNs) have recently achieved impressive results in a variety of application areas including visual question answering, trajectory prediction, object tracking, and language modelling.
Forecasting Future Action Sequences with Neural Memory Networks
We propose a novel neural memory network based framework for future action sequence forecasting.
Coupled Generative Adversarial Network for Continuous Fine-grained Action Segmentation
The goal of both GANs is to generate similar `action codes', a vector representation of the current action.
Fine-grained Action Segmentation using the Semi-Supervised Action GAN
In this paper we address the problem of continuous fine-grained action segmentation, in which multiple actions are present in an unsegmented video stream.
Rethinking Planar Homography Estimation Using Perspective Fields
In addition, the new parameterization of this task is general and can be implemented by any fully convolutional network (FCN) architecture.
Ranked #1 on
Homography Estimation
on COCO 2014
Improved Reinforcement Learning with Curriculum
Humans tend to learn complex abstract concepts faster if examples are presented in a structured manner.
Geometry-constrained Car Recognition Using a 3D Perspective Network
Unlike existing methods which only use attention mechanisms to locate 2D discriminative information, our work learns a novel 3D perspective feature representation of a vehicle, which is then fused with 2D appearance feature to predict the category.
MTRNet: A Generic Scene Text Eraser
Developing such a generic text eraser for real scenes is a challenging task, since it inherits all the challenges of multi-lingual and curved text detection and inpainting.
Memory Augmented Deep Generative models for Forecasting the Next Shot Location in Tennis
This paper presents a novel framework for predicting shot location and type in tennis.
A Deep Four-Stream Siamese Convolutional Neural Network with Joint Verification and Identification Loss for Person Re-detection
In this paper, we propose a four stream Siamese deep convolutional neural network for person redetection that jointly optimises verification and identification losses over a four image input group.
GD-GAN: Generative Adversarial Networks for Trajectory Prediction and Group Detection in Crowds
This paper presents a novel deep learning framework for human trajectory prediction and detecting social group membership in crowds.
Multi-Level Sequence GAN for Group Activity Recognition
The generator is fed with person-level and scene-level features that are mapped temporally through LSTM networks.
Component-based Attention for Large-scale Trademark Retrieval
The demand for large-scale trademark retrieval (TR) systems has significantly increased to combat the rise in international trademark infringement.
Skeleton Driven Non-rigid Motion Tracking and 3D Reconstruction
We propose a puppet model-based tracking approach using skeleton prior, which provides a better initialization for tracking articulated movements.
Pedestrian Trajectory Prediction with Structured Memory Hierarchies
This paper presents a novel framework for human trajectory prediction based on multimodal data (video and radar).
Deep Decision Trees for Discriminative Dictionary Learning with Adversarial Multi-Agent Trajectories
With the explosion in the availability of spatio-temporal tracking data in modern sports, there is an enormous opportunity to better analyse, learn and predict important events in adversarial group environments.
Learning Temporal Strategic Relationships using Generative Adversarial Imitation Learning
This paper presents a novel framework for automatic learning of complex strategies in human decision making.
Tracking by Prediction: A Deep Generative Model for Mutli-Person localisation and Tracking
We present a novel, complete deep learning framework for multi-person localisation and tracking.
Generative Adversarial Network
Pedestrian Trajectory Prediction
+2
Task Specific Visual Saliency Prediction with Memory Augmented Conditional Generative Adversarial Networks
Visual saliency patterns are the result of a variety of factors aside from the image being parsed, however existing approaches have ignored these.
Joint Max Margin and Semantic Features for Continuous Event Detection in Complex Scenes
In this paper the problem of complex event detection in the continuous domain (i. e. events with unknown starting and ending locations) is addressed.
Two Stream LSTM: A Deep Fusion Framework for Human Action Recognition
Our contribution in this paper is a deep fusion framework that more effectively exploits spatial features from CNNs with temporal features from LSTM models.
Tree Memory Networks for Modelling Long-term Temporal Dependencies
In this paper, we propose a Tree Memory Network (TMN) for modelling long term and short term relationships in sequence-to-sequence mapping problems.
Soft + Hardwired Attention: An LSTM Framework for Human Trajectory Prediction and Abnormal Event Detection
We illustrate how a simple approximation of attention weights (i. e hard-wired) can be merged together with soft attention weights in order to make our model applicable for challenging real world scenarios with hundreds of neighbours.
Learning Temporal Alignment Uncertainty for Efficient Event Detection
A popular approach in this regard is to represent a sequence using a bag of words (BOW) representation due to its: (i) fixed dimensionality irrespective of the sequence length, and (ii) its ability to compactly model the statistics in the sequence.
