Deep neural networks (DNNs) have been deployed for many image segmentation
tasks and achieved outstanding performance. However, preparing a dataset for
training segmentation DNNs is laborious and costly since typically pixel-level
annotations are provided for each object of interest. To alleviate this issue,
one can provide only weak labels such as bounding boxes or scribbles, or less
accurate (noisy) annotations of the objects. These are significantly faster to
generate and thus result in more annotated images given the same time budget.
However, the reduction in quality might negatively affect the segmentation
performance of the resulting model. In this study, we perform a thorough
cost-effectiveness evaluation of several weak and noisy labels. We considered
11 variants of annotation strategies and 4 datasets. We conclude that the
common practice of accurately outlining the objects of interest is virtually
never the optimal approach when the annotation time is limited, even if notable
annotation time is available (10s of hours). Annotation approaches that stood
out in such scenarios were (1) polygon-based annotation with few vertices, and
(2) box annotations combined with the Segment Anything Model (SAM). In
situations where unlimited annotation time was available, precise annotations
still lead to the highest segmentation model performance.

通过对 11 种注释策略和 4 个数据集进行彻底的成本效益评估，我们得出结论：当注释时间有限时，准确地勾绘感兴趣的对象通常不是最佳方法，注释时间越多也是如此。在这种情况下，突出显示的注释方法是（1）具有少量顶点的基于多边形的注释，以及（2）与 “Segment Anything Model（SAM）” 结合的框注释。在有无限注释时间的情况下，精确的注释仍然会导致最高的分割模型性能。