  Acquiring count annotations generally requires less human effort than
point-level and bounding box annotations. Thus, we propose the novel problem
setup of localizing objects in dense scenes under this weaker supervision. We
propose LOOC, a method to Localize Overlapping Objects with Count supervision.
We train LOOC by alternating between two stages. In the first stage, LOOC
learns to generate pseudo point-level annotations in a semi-supervised manner.
In the second stage, LOOC uses a fully-supervised localization method that
trains on these pseudo labels. The localization method is used to progressively
improve the quality of the pseudo labels. We conducted experiments on popular
counting datasets. For localization, LOOC achieves a strong new baseline in the
novel problem setup where only count supervision is available. For counting,
LOOC outperforms current state-of-the-art methods that only use count as their
supervision. Code is available at: https://github.com/ElementAI/looc.
