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cs.CV2020
Scalable learning for bridging the species gap in image-based plant phenotyping
Daniel Ward, Peyman Moghadam
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 sp…
cs.CV2020
Temporally Coherent Embeddings for Self-Supervised Video Representation Learning
Joshua Knights, Ben Harwood, Daniel Ward +3
This paper presents TCE: Temporally Coherent Embeddings for self-supervised video representation learning. The proposed method exploits inherent structure of unlabeled video data t…
cs.CV2018
Deep Leaf Segmentation Using Synthetic Data
Daniel Ward, Peyman Moghadam, Nicolas Hudson
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…