Clinton Fookes

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Professor

Queensland University of Technology

Australia

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About

Professor Clinton Fookes is a faculty member at the Queensland University of Technology in Australia. His research focuses on advanced topics in machine learning, medical image analysis, and computer vision, as evidenced by his recent publications. Notable areas of study include predictive modeling, few-shot learning, and physics-informed approaches to both reinforcement learning and medical image segmentation.

Recent Grants

Grant: Close

One shot three-dimensional reconstruction of human anatomy and motion

Open Date: 2017-01-01

Close Date: 2019-12-31

Grant: Close

The challenge of alignment distortion and creating automatic facial expression detection

Open Date: 2014-01-01

Close Date: 2016-12-31

Grant: Close

Improving the productivity and efficiency of Australian airports

Open Date: 2014-01-01

Close Date: 2017-12-31

Grant: Close

Monitoring intuitive expertise in the context of airport security screening

Open Date: 2014-01-01

Close Date: 2017-12-31

Grant: Close

Integrated command and control facility for large-scale critical infrastructure management

Open Date: 2011-01-01

Close Date: 2011-12-31

Clinton Fookes

Research Interests

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About

Recent Grants

Articles (25)

Few-Shot Learning for Medical Image Segmentation: A Review and Comparative Study

Enhancing predictive performance on long-tail trajectories via clustering and specialized decoders

Physics-informed Machine Learning for Medical Image Analysis

A survey on physics informed reinforcement learning: Review and open problems

Remembering What is Important: A Factorised Multi-Head Retrieval and Auxiliary Memory Stabilisation Scheme for Human Motion Prediction

Decoupled and Explainable Associative Memory for Effective Knowledge Propagation

An Online Harmonic Estimation Technique Based on Deep Learning in Distribution Networks

GeoAdapt: Self-Supervised Test-Time Adaptation in LiDAR Place Recognition Using Geometric Priors

AG-ReID.v2: Bridging Aerial and Ground Views for Person Re-Identification

FactoFormer: Factorized Hyperspectral Transformers With Self-Supervised Pretraining

Multimodal Colearning Meets Remote Sensing: Taxonomy, State of the Art, and Future Works

Physical Adversarial Attacks for Surveillance: A Survey

Synthetic Data for Deep Learning in Computer Vision & Medical Imaging: A Means to Reduce Data Bias

Physics-Informed Computer Vision: A Review and Perspectives

Physics Augmented Tuple Transformer for Autism Severity-Level Detection

Generalized Generative Deep Learning Models for Biosignal Synthesis and Modality Transfer

Complex-Valued Iris Recognition Network

DConv-LSTM-Net: A Novel Architecture for Single and 12-Lead ECG Anomaly Detection.

Spectral Geometric Verification: Re-Ranking Point Cloud Retrieval for Metric Localization

Toward On-Board Panoptic Segmentation of Multispectral Satellite Images

Continuous Human Action Recognition for Human-machine Interaction: A Review

Multi-stage stacked temporal convolution neural networks (MS-S-TCNs) for biosignal segmentation and anomaly localization

Learning test-time augmentation for content-based image retrieval

Robust and Interpretable Temporal Convolution Network for Event Detection in Lung Sound Recordings

Elasticity Meets Continuous-Time: Map-Centric Dense 3D LiDAR SLAM

Collaborators (11)

Zongyuan Ge

Osman Tursun

Truyen Tran

Olivier Salvado

Sridha Sridharan

Harshala Gammulle

Peyman Moghadam

Chayan Banerjee

Arun Ross

Kien Nguyen Thanh

Maryam Haghighat

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