Search Results for author: Simon Denman

Found 56 papers, 4 papers with code

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

Towards Interpretable Attention Networks for Cervical Cancer Analysis

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

Here, we evaluate various state-of-the-art deep learning models and attention-based frameworks for the classification of images of multiple cervical cells.

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

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

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

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

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

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

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.

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

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.

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

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

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

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.

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

Improved Reinforcement Learning with Curriculum

no code implementations29 Mar 2019 Joseph West, Frederic Maire, Cameron Browne, Simon Denman

Humans tend to learn complex abstract concepts faster if examples are presented in a structured manner.

Board Games

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

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.

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

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

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

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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