Daniel Ward

Daniel Ward
The University of Queensland | UQ · School of Information Technology and Electrical Engineering

2.26
 · 
BEng (Hons), Electrical and Computer Engineering

About

5
Publications
979
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15
Citations
Introduction
Daniel Ward currently studies at the School of Information Technology and Electrical Engineering, The University of Queensland. Daniel does research in Computer Engineering, Artificial Neural Network and Algorithms.

Publications

Publications (5)
Article
The traditional paradigm of applying deep learning -collect, annotate and train on data- is not applicable to image-based plant phenotyping. Data collection involves the growth of many physical samples, imaging them at multiple growth stages and finally manually annotating each image. This process is error-prone, expensive, time consuming and often...
Preprint
The traditional paradigm of applying deep learning -- collect, annotate and train on data -- is not applicable to image-based plant phenotyping as almost 400,000 different plant species exists. Data costs include growing physical samples, imaging and labelling them. Model performance is impacted by the species gap between the domain of each plant s...
Preprint
This paper presents TCE: Temporally Coherent Embeddings for self-supervised video representation learning. The proposed method exploits inherent structure of unlabeled video data to explicitly enforce temporal coherency in the embedding space, rather than indirectly learning it through ranking or predictive pretext tasks. In the same way that high-...
Preprint
Full-text available
Automated segmentation of individual leaves of a plant in an image is a prerequisite to measure more complex phenotypic traits in high-throughput phenotyping. Applying state-of-the-art machine learning approaches to tackle leaf instance segmentation requires a large amount of manually annotated training data. Currently, the benchmark datasets for l...

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