Self-supervised Learning for Panoptic Segmentation of Multiple Fruit Flower Species. (arXiv:2209.04618v1 [cs.CV])

Convolutional neural networks trained using manually generated labels are
commonly used for semantic or instance segmentation. In precision agriculture,
automated flower detection methods use supervised models and post-processing
techniques that may not perform consistently as the appearance of the flowers
and the data acquisition conditions vary. We propose a self-supervised learning
strategy to enhance the sensitivity of segmentation models to different flower
species using automatically generated pseudo-labels. We employ a data
augmentation and refinement approach to improve the accuracy of …

arxiv flower segmentation self-supervised learning supervised learning