no code implementations • 17 Aug 2023 • Harshala Gammulle, Yubo Chen, Sridha Sridharan, Travis Klein, Clinton Fookes
However, there is a lack of focus on developing lightweight models which can run in low-resource environments, which are typically encountered in medical clinics.
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 • 8 Aug 2022 • Pengbo Wei, David Ahmedt-Aristizabal, Harshala Gammulle, Simon Denman, Mohammad Ali Armin
Advances in machine learning and contactless sensors have enabled the understanding complex human behaviors in a healthcare setting.
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 • 26 Feb 2022 • Harshala Gammulle, David Ahmedt-Aristizabal, Simon Denman, Lachlan Tychsen-Smith, Lars Petersson, Clinton Fookes
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.
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 • 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 • 10 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.
no code implementations • 12 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.
no code implementations • 7 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.
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.
no code implementations • 20 Sep 2019 • Harshala Gammulle, Simon Denman, Sridha Sridharan, Clinton Fookes
We propose a novel neural memory network based framework for future action sequence forecasting.
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 • 20 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.
no code implementations • 18 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.
no code implementations • 4 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.