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Found 2 papers, 2 papers with code
Weakly-Supervised Cross-Domain Segmentation of Electron Microscopy with Sparse Point Annotation
To address these issues, we investigate a highly annotation-efficient weak supervision, which assumes only sparse center-points on a small subset of object instances in the target training images.
WDA-Net: Weakly-Supervised Domain Adaptive Segmentation of Electron Microscopy
Aiming to develop a highly annotation-efficient approach with competitive performance, we focus on weakly-supervised domain adaptation (WDA) with a type of extremely sparse and weak annotation demanding minimal annotation efforts, i. e., sparse point annotations on only a small subset of object instances.
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