Dr Harshala Gammulle
Faculty of Engineering,
School of Electrical Engineering & Robotics
Biography
Dr. Harshala Gammulle is a Research Fellow in the Signal Processing, Artificial Intelligence, and Vision Technologies (SAIVT) research program in the school of Electrical Engineering and Robotics at Queensland University of Technology (QUT). She received her BSc (special degree in computer science) from the University of Peradeniya, Sri Lanka, and her PhD from QUT, Australia. Her current research goals include developing artificial intelligence techniques for better understanding and representing events from visual inputs.During her PhD, Harshala proposed machine learning techniques for understanding human behavior in videos in multiple problem settings including, recognition, segmentation, and prediction. Her research to date has resulted in significant contributions to a diverse set of application domains including, security surveillance, sports action recognition, group activity recognition, and recognition of cooking activities. In recognition of the significant contributions to the knowledge base in her field of research, Harshala was awarded the QUT Executive Dean's Commendation for Outstanding Doctoral Thesis Award.
Since the completion of her PhD, Harshala is continuing her research activities in the computer vision domain as one of the project members from QUT in the Rheinmetall Australia Research and Technology Program, which is their first research and technology program in Australia. Furthermore, Harshala is conducting interdisciplinary research activities by extending the applications of her proposed techniques to healthcare.
Publication highlights
- "Predicting the Future: A Jointly Learnt Model for Action Anticipation." In Proceedings of the IEEE International Conference on Computer Vision (ICCV) 2019.
- "Fine-grained action segmentation using the semi-supervised action GAN." Pattern Recognition 98 (2020).
- "Forecasting future action sequences with neural memory networks." In British Machine Vision Conference (BMVC) 2019.
- "Coupled generative adversarial network for continuous fine-grained action segmentation." In 2019 IEEE Winter Conference on Applications of Computer Vision (WACV).
- "Multi-level sequence GAN for group activity recognition." In Asian Conference on Computer Vision (ACCV) 2018.
Personal details
Positions
- Research Fellow
Faculty of Engineering,
School of Electrical Engineering & Robotics
Keywords
Computer Vision, Image Processing, Pattern Recognition, Deep Learning, Machine Learning, Human Action Recognition and Prediction, Medical Image Analysis
Discipline
Artificial Intelligence and Image Processing
Field of Research code, Australian and New Zealand Standard Research Classification (ANZSRC), 2008
Qualifications
- Doctor of Philosophy (Queensland University of Technology)
Selected publications
- Fernando T, Gammulle H, Denman S, Sridharan S, Fookes C, (2022) Deep Learning for Medical Anomaly Detection: A Survey, ACM Computing Surveys, 54 (7).
- Gammulle H, Denman S, Sridharan S, Fookes C, (2021) TMMF: Temporal Multi-modal Fusion for Single-Stage Continuous Gesture Recognition, IEEE Transactions on Image Processing, 30, pp. 7689-7701.
- Gammulle H, Denman S, Sridharan S, Fookes C, (2020) Fine-grained action segmentation using the semi-supervised action GAN, Pattern Recognition, 98.
- Gammulle H, Denman S, Sridharan S, Fookes C, (2020) Hierarchical Attention Network for Action Segmentation, Pattern Recognition Letters, 131, pp. 442-448.
- Gammulle H, Denman S, Sridharan S, Fookes C, (2020) Two-stream deep feature modelling for automated video endoscopy data analysis, Medical Image Computing and Computer Assisted Intervention – MICCAI 2020: 23rd International Conference, Proceedings, Part III, pp. 742-751.
- Gammulle P, Warnakulasuriya T, Denman S, Sridharan S, Fookes C, (2019) Coupled generative adversarial network for continuous fine-grained action segmentation, Proceedings of the 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 200-209.
- Gammulle P, Denman S, Sridharan S, Fookes C, (2019) Forecasting Future Action Sequences with Neural Memory Networks, Proceedings of the 30th British Machine Vision Conference 2019, BMVC 201, pp. 1-12.
- Gammulle P, Denman S, Sridharan S, Fookes C, (2019) Multi-level sequence GAN for group activity recognition, Computer Vision – ACCV 2018 - 14th Asian Conference on Computer Vision, Revised Selected Papers, pp. 331-346.
- Gammulle H, Denman S, Sridharan S, Fookes C, (2019) Predicting the future: A jointly learnt model for action anticipation, Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision (ICCV 2019) , pp. 5561-5570.
- Gammulle P, Denman S, Sridharan S, Fookes C, (2017) Two stream LSTM: A deep fusion framework for human action recognition, Proceedings of the 2017 IEEE Winter Conference on Applications of Computer Vision (WACV 2017), pp. 177-186.
QUT ePrints
For more publications by Harshala, explore their research in QUT ePrints (our digital repository).
Awards
- Type
- Academic Honours, Prestigious Awards or Prizes
- Reference year
- 2020
- Details
- QUT Executive Dean's Commendation for Outstanding Doctoral Thesis Award
- Type
- Academic Honours, Prestigious Awards or Prizes
- Reference year
- 2015
- Details
- University Award for Academic Excellence, University of Peradeniya, Sri Lanka
- Type
- Other
- Reference year
- 2021
- Details
- WiT Emerging Achiever Technology Award - Finalist
Supervision
Current supervisions
- Tracking Players in Sport Without Appearance Information
PhD, Associate Supervisor
Other supervisors: Professor Sridha Sridharan, Professor Clinton Fookes