1 code implementation • 18 Sep 2023 • Shaheer Mohamed, Maryam Haghighat, Tharindu Fernando, Sridha Sridharan, Clinton Fookes, Peyman Moghadam
However, current state-of-the-art hyperspectral transformers only tokenize the input HSI sample along the spectral dimension, resulting in the under-utilization of spatial information.
no code implementations • 19 May 2023 • Tharindu Fernando, Harshala Gammulle, Sridha Sridharan, Simon Denman, Clinton Fookes
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.
no code implementations • 1 May 2023 • Kien Nguyen, Tharindu Fernando, Clinton Fookes, Sridha Sridharan
In particular, we propose a framework to analyze physical adversarial attacks and provide a comprehensive survey of physical adversarial attacks on four key surveillance tasks: detection, identification, tracking, and action recognition under this framework.
no code implementations • 15 Nov 2022 • Tharindu Fernando, Clinton Fookes, Sridha Sridharan, Dana Michalski
Person re-identification (re-id) is a pivotal task within an intelligent surveillance pipeline and there exist numerous re-id frameworks that achieve satisfactory performance in challenging benchmarks.
no code implementations • 5 Apr 2022 • Tharindu Fernando, Clinton Fookes, Harshala Gammulle, Simon Denman, Sridha Sridharan
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.
no code implementations • 9 Aug 2021 • Harshala Gammulle, Tharindu Fernando, Sridha Sridharan, Simon Denman, Clinton Fookes
This paper presents a novel lightweight COVID-19 diagnosis framework using CT scans.
no code implementations • 30 Jun 2021 • Tharindu Fernando, Sridha Sridharan, Simon Denman, Houman Ghaemmaghami, Clinton Fookes
We exceed the state-of-the-art results in all evaluations.
no code implementations • 4 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.
no code implementations • 18 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.
no code implementations • 12 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.
no code implementations • 10 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.
no code implementations • 23 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.
no code implementations • 16 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.
no code implementations • 21 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.
no code implementations • 2 Apr 2020 • Tharindu Fernando, Sridha Sridharan, Mitchell McLaren, Darshana Priyasad, Simon Denman, Clinton Fookes
This paper presents a novel framework for Speech Activity Detection (SAD).
no code implementations • 2 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.
no code implementations • 10 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.
no code implementations • 17 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.
no code implementations • 12 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.
no code implementations • 20 Sep 2019 • Harshala Gammulle, Tharindu Fernando, Simon Denman, Sridha Sridharan, Clinton Fookes
The goal of both GANs is to generate similar `action codes', a vector representation of the current action.
no code implementations • 16 Jan 2019 • Tharindu Fernando, Simon Denman, Sridha Sridharan, Clinton Fookes
This paper presents a novel framework for predicting shot location and type in tennis.
no code implementations • 18 Dec 2018 • Tharindu Fernando, Simon Denman, Sridha Sridharan, Clinton Fookes
This paper presents a novel deep learning framework for human trajectory prediction and detecting social group membership in crowds.
no code implementations • 22 Jul 2018 • Tharindu Fernando, Simon Denman, Sridha Sridharan, Clinton Fookes
This paper presents a novel framework for human trajectory prediction based on multimodal data (video and radar).
no code implementations • 14 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.
no code implementations • 13 May 2018 • Tharindu Fernando, Simon Denman, Sridha Sridharan, Clinton Fookes
This paper presents a novel framework for automatic learning of complex strategies in human decision making.
no code implementations • 9 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.
no code implementations • 9 Mar 2018 • Tharindu Fernando, Simon Denman, Sridha Sridharan, Clinton Fookes
We present a novel, complete deep learning framework for multi-person localisation and tracking.
Generative Adversarial Network Pedestrian Trajectory Prediction +2
no code implementations • 12 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.
no code implementations • 18 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.