Survey of segmentation when there are few examples
Survey of segmentation when there are few examples
Few Shot Semantic Segmentation: a review of methodologies and open challenges
arXiv paper abstract https://arxiv.org/abs/2304.05832
arXiv PDF paper https://arxiv.org/pdf/2304.05832.pdf
Semantic segmentation assigns category labels to each pixel in an image, enabling breakthroughs in fields such as autonomous driving and robotics.
Deep Neural Networks have achieved high accuracies in semantic segmentation but require large training datasets.
Some domains have difficulties building such datasets due to rarity, privacy concerns, and the need for skilled annotators.
Few-Shot Learning (FSL) has emerged as a new research stream that allows models to learn new tasks from a few samples.
This contribution provides an overview of FSL in semantic segmentation (FSS), proposes a new taxonomy, and describes current limitations and outlooks.
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