# Full or Weak annotations? An adaptive strategy for budget-constrained annotation campaigns Source: [Full or Weak annotations? An adaptive strategy for budget-constrained annotation campaigns]() Collection index: [llms.txt]() Data updated: 2026-01-04T04:30:21.408Z Authors: Javier Gamazo Tejero, Martin S. Zinkernagel, Sebastian Wolf, Raphael Sznitman, Pablo M\\'arquez Neila Published: 2023-03-21T08:41:54.000Z ## Abstract Annotating new datasets for machine learning tasks is tedious, time-consuming, and costly. For segmentation applications, the burden is particularly high as manual delineations of relevant image content are often extremely expensive or can only be done by experts with domain-specific knowledge. Thanks to developments in transfer learning and training with weak supervision, segmentation models can now also greatly benefit from annotations of different kinds. However, for any new domain application looking to use weak supervision, the dataset builder still needs to define a strategy to distribute full segmentation and other weak annotations. Doing so is challenging, however, as it is a priori unknown how to distribute an annotation budget for a given new dataset. To this end, we propose a novel approach to determine annotation strategies for segmentation datasets, whereby estimating what proportion of segmentation and classification annotations should be collected given a fixed budget. To do so, our method sequentially determines proportions of segmentation and classification annotations to collect for budget-fractions by modeling the expected improvement of the final segmentation model. We show in our experiments that our approach yields annotations that perform very close to the optimal for a number of different annotation budgets and datasets. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Semantic Segmentation on SUIM (test)](): Semantic segmentation; SUIM (test); Relative Performance: 81.9. - [Semantic Segmentation on VOC (test)](): Semantic segmentation; VOC (test); Relative Performance: 46. - [Semantic Segmentation on OCT (test)](): Semantic segmentation; OCT (test); Relative Performance: 92.6. - [Semantic Segmentation on Cityscapes (test)](): Semantic segmentation; Cityscapes (test); mIoU: not recorded. ## Original sources and implementation [Original paper]() [PDF]()