and COCO prove that the proposed prior generation method and FEM both improve the baseline method significantly. Our PFENet also outperforms state-of-the-art methods by a large margin without efficiency loss. It is surprising that our model even generalizes to cases without labeled support samples.

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Cite This Study

Tian et al. (Mon,) studied this question.

synapsesocial.com/papers/69fff8476018b8d0892da227 https://doi.org/https://doi.org/10.1109/tpami.2020.3013717

Also Consider

Synapse has enriched 2 closely related papers on similar clinical questions. Consider them for comparative context:

1CANet: Class-Agnostic Segmentation Networks With Iterative Refinement and Attentive Few-Shot Learning2019 · 662 citations
2U-Net: Convolutional Networks for Biomedical Image Segmentation2015 · 88,957 citations

Also Consider

Synapse has enriched 2 closely related papers on similar clinical questions. Consider them for comparative context:

1CANet: Class-Agnostic Segmentation Networks With Iterative Refinement and Attentive Few-Shot Learning2019 · 662 citations
2U-Net: Convolutional Networks for Biomedical Image Segmentation2015 · 88,957 citations

Prior Guided Feature Enrichment Network for Few-Shot Segmentation | Synapse