Search Results for author: Clinton Fookes

Found 83 papers, 7 papers with code

The State of Aerial Surveillance: A Survey

no code implementations9 Jan 2022 Kien Nguyen, Clinton Fookes, Sridha Sridharan, YingLi Tian, Feng Liu, Xiaoming Liu, Arun Ross

The rapid emergence of airborne platforms and imaging sensors are enabling new forms of aerial surveillance due to their unprecedented advantages in scale, mobility, deployment and covert observation capabilities.

Point Cloud Segmentation Using Sparse Temporal Local Attention

no code implementations1 Dec 2021 Joshua Knights, Peyman Moghadam, Clinton Fookes, Sridha Sridharan

Point clouds are a key modality used for perception in autonomous vehicles, providing the means for a robust geometric understanding of the surrounding environment.

Autonomous Vehicles Point Cloud Segmentation

CorticalFlow: A Diffeomorphic Mesh Transformer Network for Cortical Surface Reconstruction

no code implementations NeurIPS 2021 Leo Lebrat, Rodrigo Santa Cruz, Frederic de Gournay, Darren Fu, Pierrick Bourgeat, Jurgen Fripp, Clinton Fookes, Olivier Salvado

In this paper, we introduce CorticalFlow, a new geometric deep-learning model that, given a 3-dimensional image, learns to deform a reference template towards a targeted object.

Surface Reconstruction

LoGG3D-Net: Locally Guided Global Descriptor Learning for 3D Place Recognition

no code implementations17 Sep 2021 Kavisha Vidanapathirana, Milad Ramezani, Peyman Moghadam, Sridha Sridharan, Clinton Fookes

Retrieval-based place recognition is an efficient and effective solution for enabling re-localization within a pre-built map or global data association for Simultaneous Localization and Mapping (SLAM).

Simultaneous Localization and Mapping

Discriminative Domain-Invariant Adversarial Network for Deep Domain Generalization

no code implementations20 Aug 2021 Mohammad Mahfujur Rahman, Clinton Fookes, Sridha Sridharan

Domain generalization approaches aim to learn a domain invariant prediction model for unknown target domains from multiple training source domains with different distributions.

Domain Generalization

A Survey on Graph-Based Deep Learning for Computational Histopathology

no code implementations1 Jul 2021 David Ahmedt-Aristizabal, Mohammad Ali Armin, Simon Denman, Clinton Fookes, Lars Petersson

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.

graph construction Image Retrieval +3

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future

no code implementations27 May 2021 David Ahmedt-Aristizabal, Mohammad Ali Armin, Simon Denman, Clinton Fookes, Lars Petersson

It has become critical to explore how machine learning and specifically deep learning methods can be exploited to analyse healthcare data.

Medical Diagnosis

MongeNet: Efficient Sampler for Geometric Deep Learning

1 code implementation CVPR 2021 Léo Lebrat, Rodrigo Santa Cruz, Clinton Fookes, Olivier Salvado

Recent advances in geometric deep-learning introduce complex computational challenges for evaluating the distance between meshes.

Semantic Consistency and Identity Mapping Multi-Component Generative Adversarial Network for Person Re-Identification

no code implementations28 Apr 2021 Amena Khatun, Simon Denman, Sridha Sridharan, Clinton Fookes

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.

Person Re-Identification

Deep Domain Generalization with Feature-norm Network

no code implementations28 Apr 2021 Mohammad Mahfujur Rahman, Clinton Fookes, Sridha Sridharan

To tackle the aforementioned problem, we introduce an end-to-end feature-norm network (FNN) which is robust to negative transfer as it does not need to match the feature distribution among the source domains.

Domain Generalization Image Classification

Preserving Semantic Consistency in Unsupervised Domain Adaptation Using Generative Adversarial Networks

no code implementations28 Apr 2021 Mohammad Mahfujur Rahman, Clinton Fookes, Sridha Sridharan

This network can achieve source to target domain matching by capturing semantic information at the feature level and producing images for unsupervised domain adaptation from both the source and the target domains.

Unsupervised Domain Adaptation

Pose-driven Attention-guided Image Generation for Person Re-Identification

no code implementations28 Apr 2021 Amena Khatun, Simon Denman, Sridha Sridharan, Clinton Fookes

Person re-identification (re-ID) concerns the matching of subject images across different camera views in a multi camera surveillance system.

Person Re-Identification Pose Transfer +1

Learning Regional Attention over Multi-resolution Deep Convolutional Features for Trademark Retrieval

no code implementations15 Apr 2021 Osman Tursun, Simon Denman, Sridha Sridharan, Clinton Fookes

However, R-MAC suffers in the presence of background clutter/trivial regions and scale variance, and discards important spatial information.

Content-Based Image Retrieval Trademark Retrieval

An Efficient Framework for Zero-Shot Sketch-Based Image Retrieval

no code implementations8 Feb 2021 Osman Tursun, Simon Denman, Sridha Sridharan, Ethan Goan, Clinton Fookes

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.

Content-Based Image Retrieval Domain Adaptation +3

Deep Learning for Medical Anomaly Detection -- A Survey

no code implementations4 Dec 2020 Tharindu Fernando, Harshala Gammulle, Simon Denman, Sridha Sridharan, Clinton Fookes

Machine learning-based medical anomaly detection is an important problem that has been extensively studied.

Anomaly Detection

Complex-valued Iris Recognition Network

no code implementations23 Nov 2020 Kien Nguyen, Clinton Fookes, Sridha Sridharan, Arun Ross

Unlike the problem of general object recognition, where real-valued neural networks can be used to extract pertinent features, iris recognition depends on the extraction of both phase and magnitude information from the input iris texture in order to better represent its biometric content.

Iris Recognition Object Recognition

Patient-independent Epileptic Seizure Prediction using Deep Learning Models

no code implementations18 Nov 2020 Theekshana Dissanayake, Tharindu Fernando, Simon Denman, Sridha Sridharan, Clinton Fookes

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.

EEG Seizure prediction

Domain Generalization in Biosignal Classification

no code implementations12 Nov 2020 Theekshana Dissanayake, Tharindu Fernando, Simon Denman, Houman Ghaemmaghami, Sridha Sridharan, Clinton Fookes

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.

Domain Generalization General Classification

Fast & Slow Learning: Incorporating Synthetic Gradients in Neural Memory Controllers

no code implementations10 Nov 2020 Tharindu Fernando, Simon Denman, Sridha Sridharan, Clinton Fookes

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.

Meta-Learning

Multi-modal Fusion for Single-Stage Continuous Gesture Recognition

no code implementations10 Nov 2020 Harshala Gammulle, Simon Denman, Sridha Sridharan, Clinton Fookes

Gesture recognition is a much studied research area which has myriad real-world applications including robotics and human-machine interaction.

Gesture Recognition

DeepCSR: A 3D Deep Learning Approach for Cortical Surface Reconstruction

no code implementations22 Oct 2020 Rodrigo Santa Cruz, Leo Lebrat, Pierrick Bourgeat, Clinton Fookes, Jurgen Fripp, Olivier Salvado

Having these limitations in mind, we propose DeepCSR, a 3D deep learning framework for cortical surface reconstruction from MRI.

Surface Reconstruction

Attention Driven Fusion for Multi-Modal Emotion Recognition

no code implementations23 Sep 2020 Darshana Priyasad, Tharindu Fernando, Simon Denman, Clinton Fookes, Sridha Sridharan

In this paper, we present a deep learning-based approach to exploit and fuse text and acoustic data for emotion classification.

Emotion Classification Emotion Recognition

Going deeper with brain morphometry using neural networks

no code implementations7 Sep 2020 Rodrigo Santa Cruz, Léo Lebrat, Pierrick Bourgeat, Vincent Doré, Jason Dowling, Jurgen Fripp, Clinton Fookes, Olivier Salvado

Brain morphometry from magnetic resonance imaging (MRI) is a consolidated biomarker for many neurodegenerative diseases.

Memory based fusion for multi-modal deep learning

no code implementations16 Jul 2020 Darshana Priyasad, Tharindu Fernando, Simon Denman, Sridha Sridharan, Clinton Fookes

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

no code implementations12 Jul 2020 Harshala Gammulle, Simon Denman, Sridha Sridharan, Clinton Fookes

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.

Bayesian Neural Networks: An Introduction and Survey

no code implementations22 Jun 2020 Ethan Goan, Clinton Fookes

Neural Networks (NNs) have provided state-of-the-art results for many challenging machine learning tasks such as detection, regression and classification across the domains of computer vision, speech recognition and natural language processing.

Speech Recognition

A Robust Interpretable Deep Learning Classifier for Heart Anomaly Detection Without Segmentation

no code implementations21 May 2020 Theekshana Dissanayake, Tharindu Fernando, Simon Denman, Sridha Sridharan, Houman Ghaemmaghami, Clinton Fookes

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.

Anomaly Detection Explainable artificial intelligence +1

Hierarchical Attention Network for Action Segmentation

no code implementations7 May 2020 Harshala Gammulle, Simon Denman, Sridha Sridharan, Clinton Fookes

The temporal segmentation of events is an essential task and a precursor for the automatic recognition of human actions in the video.

Action Segmentation

End-to-End Domain Adaptive Attention Network for Cross-Domain Person Re-Identification

no code implementations7 May 2020 Amena Khatun, Simon Denman, Sridha Sridharan, Clinton Fookes

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.

Person Re-Identification Translation

Deep Auto-Encoders with Sequential Learning for Multimodal Dimensional Emotion Recognition

no code implementations28 Apr 2020 Dung Nguyen, Duc Thanh Nguyen, Rui Zeng, Thanh Thi Nguyen, Son N. Tran, Thin Nguyen, Sridha Sridharan, Clinton Fookes

Multimodal dimensional emotion recognition has drawn a great attention from the affective computing community and numerous schemes have been extensively investigated, making a significant progress in this area.

Emotion Recognition

Heart Sound Segmentation using Bidirectional LSTMs with Attention

no code implementations2 Apr 2020 Tharindu Fernando, Houman Ghaemmaghami, Simon Denman, Sridha Sridharan, Nayyar Hussain, Clinton Fookes

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.

A Multiple Decoder CNN for Inverse Consistent 3D Image Registration

no code implementations15 Feb 2020 Abdullah Nazib, Clinton Fookes, Olivier Salvado, Dimitri Perrin

The recent application of deep learning technologies in medical image registration has exponentially decreased the registration time and gradually increased registration accuracy when compared to their traditional counterparts.

Image Registration Medical Image Registration

Spatiotemporal Camera-LiDAR Calibration: A Targetless and Structureless Approach

no code implementations17 Jan 2020 Chanoh Park, Peyman Moghadam, Soohwan Kim, Sridha Sridharan, Clinton Fookes

The demand for multimodal sensing systems for robotics is growing due to the increase in robustness, reliability and accuracy offered by these systems.

MTRNet++: One-stage Mask-based Scene Text Eraser

1 code implementation16 Dec 2019 Osman Tursun, Simon Denman, Rui Zeng, Sabesan Sivapalan, Sridha Sridharan, Clinton Fookes

The results of ablation studies demonstrate that the proposed multi-branch architecture with attention blocks is effective and essential.

Predicting the Future: A Jointly Learnt Model for Action Anticipation

no code implementations ICCV 2019 Harshala Gammulle, Simon Denman, Sridha Sridharan, Clinton Fookes

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.

Action Anticipation

Neural Memory Networks for Seizure Type Classification

no code implementations10 Dec 2019 David Ahmedt-Aristizabal, Tharindu Fernando, Simon Denman, Lars Petersson, Matthew J. Aburn, Clinton Fookes

Inspired by recent advances in neural memory networks (NMNs), we introduce a novel approach for the classification of seizure type using electrophysiological data.

EEG General Classification +1

Correlation-aware Adversarial Domain Adaptation and Generalization

no code implementations29 Nov 2019 Mohammad Mahfujur Rahman, Clinton Fookes, Mahsa Baktashmotlagh, Sridha Sridharan

Domain adaptation (DA) and domain generalization (DG) have emerged as a solution to the domain shift problem where the distribution of the source and target data is different.

Domain Generalization

Exploiting Human Social Cognition for the Detection of Fake and Fraudulent Faces via Memory Networks

no code implementations17 Nov 2019 Tharindu Fernando, Clinton Fookes, Simon Denman, Sridha Sridharan

Advances in computer vision have brought us to the point where we have the ability to synthesise realistic fake content.

Face Detection

Neural Memory Plasticity for Anomaly Detection

no code implementations12 Oct 2019 Tharindu Fernando, Simon Denman, David Ahmedt-Aristizabal, Sridha Sridharan, Kristin Laurens, Patrick Johnston, Clinton Fookes

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.

Anomaly Detection EEG +5

Forecasting Future Action Sequences with Neural Memory Networks

no code implementations20 Sep 2019 Harshala Gammulle, Simon Denman, Sridha Sridharan, Clinton Fookes

We propose a novel neural memory network based framework for future action sequence forecasting.

Fine-grained Action Segmentation using the Semi-Supervised Action GAN

no code implementations20 Sep 2019 Harshala Gammulle, Simon Denman, Sridha Sridharan, Clinton Fookes

In this paper we address the problem of continuous fine-grained action segmentation, in which multiple actions are present in an unsegmented video stream.

Action Classification Action Segmentation

Rethinking Planar Homography Estimation Using Perspective Fields

1 code implementation ACCV 2018 2019 Rui Zeng, Simon Denman, Sridha Sridharan, Clinton Fookes

In addition, the new parameterization of this task is general and can be implemented by any fully convolutional network (FCN) architecture.

Homography Estimation

Constrained Design of Deep Iris Networks

no code implementations23 May 2019 Kien Nguyen, Clinton Fookes, Sridha Sridharan

On the other hand, it allows us to investigate the optimality of the classic IrisCode and recent iris networks.

Iris Recognition

Geometry-constrained Car Recognition Using a 3D Perspective Network

no code implementations19 Mar 2019 Rui Zeng, ZongYuan Ge, Simon Denman, Sridha Sridharan, Clinton Fookes

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

1 code implementation11 Mar 2019 Osman Tursun, Rui Zeng, Simon Denman, Sabesan Sivapalan, Sridha Sridharan, Clinton Fookes

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.

Curved Text Detection

On Minimum Discrepancy Estimation for Deep Domain Adaptation

no code implementations2 Jan 2019 Mohammad Mahfujur Rahman, Clinton Fookes, Mahsa Baktashmotlagh, Sridha Sridharan

In the presence of large sets of labeled data, Deep Learning (DL) has accomplished extraordinary triumphs in the avenue of computer vision, particularly in object classification and recognition tasks.

Domain Adaptation General Classification +1

Multi-component Image Translation for Deep Domain Generalization

no code implementations21 Dec 2018 Mohammad Mahfujur Rahman, Clinton Fookes, Mahsa Baktashmotlagh, Sridha Sridharan

If DA methods are applied directly to DG by a simple exclusion of the target data from training, poor performance will result for a given task.

Domain Generalization Translation

A Deep Four-Stream Siamese Convolutional Neural Network with Joint Verification and Identification Loss for Person Re-detection

no code implementations21 Dec 2018 Amena Khatun, Simon Denman, Sridha Sridharan, Clinton Fookes

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.

Person Re-Identification

Multi-Level Sequence GAN for Group Activity Recognition

no code implementations18 Dec 2018 Harshala Gammulle, Simon Denman, Sridha Sridharan, Clinton Fookes

The generator is fed with person-level and scene-level features that are mapped temporally through LSTM networks.

Action Classification Activity Prediction +2

Component-based Attention for Large-scale Trademark Retrieval

1 code implementation7 Nov 2018 Osman Tursun, Simon Denman, Sabesan Sivapalan, Sridha Sridharan, Clinton Fookes, Sandra Mau

The demand for large-scale trademark retrieval (TR) systems has significantly increased to combat the rise in international trademark infringement.

A Comparative Analysis of Registration Tools: Traditional vs Deep Learning Approach on High Resolution Tissue Cleared Data

no code implementations19 Oct 2018 Abdullah Nazib, Clinton Fookes, Dimitri Perrin

In this paper, we investigate and compare the performance of a deep learning based registration method with traditional optimization based methods on samples from tissue-clearing methods.

Image Registration

Skeleton Driven Non-rigid Motion Tracking and 3D Reconstruction

no code implementations9 Oct 2018 Shafeeq Elanattil, Peyman Moghadam, Simon Denman, Sridha Sridharan, Clinton Fookes

We propose a puppet model-based tracking approach using skeleton prior, which provides a better initialization for tracking articulated movements.

3D Reconstruction

Performance of Image Registration Tools on High-Resolution 3D Brain Images

no code implementations13 Jul 2018 Abdullah Nazib, James Galloway, Clinton Fookes, Dimitri Perrin

Recent progress in tissue clearing has allowed for the imaging of entire organs at single-cell resolution.

Image Registration

Sparse Over-complete Patch Matching

no code implementations9 Jun 2018 Akila Pemasiri, Kien Nguyen, Sridha Sridharan, Clinton Fookes

State -of-the-art patch matching techniques take image patches as input to a convolutional neural network to extract the patch features and evaluate their similarity.

Patch Matching

Non-rigid Reconstruction with a Single Moving RGB-D Camera

no code implementations29 May 2018 Shafeeq Elanattil, Peyman Moghadam, Sridha Sridharan, Clinton Fookes, Mark Cox

Our approach uses camera pose estimated from the rigid background for foreground tracking.

Meta Transfer Learning for Facial Emotion Recognition

no code implementations25 May 2018 Dung Nguyen, Kien Nguyen, Sridha Sridharan, Iman Abbasnejad, David Dean, Clinton Fookes

The use of deep learning techniques for automatic facial expression recognition has recently attracted great interest but developed models are still unable to generalize well due to the lack of large emotion datasets for deep learning.

Facial Emotion Recognition Facial Expression Recognition +1

Deep Decision Trees for Discriminative Dictionary Learning with Adversarial Multi-Agent Trajectories

no code implementations14 May 2018 Tharindu Fernando, Sridha Sridharan, Clinton Fookes, Simon Denman

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.

Dictionary Learning

Task Specific Visual Saliency Prediction with Memory Augmented Conditional Generative Adversarial Networks

no code implementations9 Mar 2018 Tharindu Fernando, Simon Denman, Sridha Sridharan, Clinton Fookes

Visual saliency patterns are the result of a variety of factors aside from the image being parsed, however existing approaches have ignored these.

Saliency Prediction

Image2Mesh: A Learning Framework for Single Image 3D Reconstruction

1 code implementation29 Nov 2017 Jhony K. Pontes, Chen Kong, Sridha Sridharan, Simon Lucey, Anders Eriksson, Clinton Fookes

One challenge that remains open in 3D deep learning is how to efficiently represent 3D data to feed deep networks.

3D Reconstruction

Elastic LiDAR Fusion: Dense Map-Centric Continuous-Time SLAM

no code implementations6 Nov 2017 Chanoh Park, Peyman Moghadam, Soohwan Kim, Alberto Elfes, Clinton Fookes, Sridha Sridharan

The concept of continuous-time trajectory representation has brought increased accuracy and efficiency to multi-modal sensor fusion in modern SLAM.

Robotics

Joint Max Margin and Semantic Features for Continuous Event Detection in Complex Scenes

no code implementations13 Jun 2017 Iman Abbasnejad, Sridha Sridharan, Simon Denman, Clinton Fookes, Simon Lucey

In this paper the problem of complex event detection in the continuous domain (i. e. events with unknown starting and ending locations) is addressed.

Action Recognition Event Detection

Two Stream LSTM: A Deep Fusion Framework for Human Action Recognition

no code implementations4 Apr 2017 Harshala Gammulle, Simon Denman, Sridha Sridharan, Clinton Fookes

Our contribution in this paper is a deep fusion framework that more effectively exploits spatial features from CNNs with temporal features from LSTM models.

Action Recognition

Tree Memory Networks for Modelling Long-term Temporal Dependencies

no code implementations12 Mar 2017 Tharindu Fernando, Simon Denman, Aaron McFadyen, Sridha Sridharan, Clinton Fookes

In this paper, we propose a Tree Memory Network (TMN) for modelling long term and short term relationships in sequence-to-sequence mapping problems.

Machine Translation Part-Of-Speech Tagging +3

Soft + Hardwired Attention: An LSTM Framework for Human Trajectory Prediction and Abnormal Event Detection

no code implementations18 Feb 2017 Tharindu Fernando, Simon Denman, Sridha Sridharan, Clinton Fookes

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.

Event Detection Machine Translation +1

Automatic Event Detection for Signal-based Surveillance

no code implementations6 Dec 2016 Jingxin Xu, Clinton Fookes, Sridha Sridharan

Though such systems are still heavily reliant on human labour to monitor the captured information, there have been a number of automatic techniques proposed to analysing the data.

Event Detection Experimental Design

Learning Temporal Alignment Uncertainty for Efficient Event Detection

no code implementations4 Sep 2015 Iman Abbasnejad, Sridha Sridharan, Simon Denman, Clinton Fookes, Simon Lucey

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.

Event Detection

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