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Annotating new datasets for machine learning tasks is tedious,time-consuming, and costly. For segmentation applications, the burden isparticularly high as manual delineations of relevant image content are oftenextremely expensive or can only be done by experts with domain-specificknowledge. Thanks to developments in transfer learning and training with weaksupervision, segmentation models can now also greatly benefit from annotationsof different kinds. However, for any new domain application looking to use weaksupervision, the dataset builder still needs to define a strategy to distributefull segmentation and other weak annotations. Doing so is challenging, however,as it is a priori unknown how to distribute an annotation budget for a givennew dataset. To this end, we propose a novel approach to determine annotationstrategies for segmentation datasets, whereby estimating what proportion ofsegmentation and classification annotations should be collected given a fixedbudget. To do so, our method sequentially determines proportions ofsegmentation and classification annotations to collect for budget-fractions bymodeling the expected improvement of the final segmentation model. We show inour experiments that our approach yields annotations that perform very close tothe optimal for a number of different annotation budgets and datasets.

Full or Weak annotations? An adaptive strategy for budget-constrained annotation campaigns