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Dense Gaussian Processes for Few-Shot Segmentation
[article]
2022
arXiv
pre-print
Few-shot segmentation is a challenging dense prediction task, which entails segmenting a novel query image given only a small annotated support set. The key problem is thus to design a method that aggregates detailed information from the support set, while being robust to large variations in appearance and context. To this end, we propose a few-shot segmentation method based on dense Gaussian process (GP) regression. Given the support set, our dense GP learns the mapping from local deep image
arXiv:2110.03674v2
fatcat:nvztetlmlfhetklfkuirfhtooe