  This paper aims to count arbitrary objects in images. The leading counting
approaches start from point annotations per object from which they construct
density maps. Then, their training objective transforms input images to density
maps through deep convolutional networks. We posit that the point annotations
serve more supervision purposes than just constructing density maps. We
introduce ways to repurpose the points for free. First, we propose supervised
focus from segmentation, where points are converted into binary maps. The
binary maps are combined with a network branch and accompanying loss function
to focus on areas of interest. Second, we propose supervised focus from global
density, where the ratio of point annotations to image pixels is used in
another branch to regularize the overall density estimation. To assist both the
density estimation and the focus from segmentation, we also introduce an
improved kernel size estimator for the point annotations. Experiments on six
datasets show that all our contributions reduce the counting error, regardless
of the base network, resulting in state-of-the-art accuracy using only a single
network. Finally, we are the first to count on WIDER FACE, allowing us to show
the benefits of our approach in handling varying object scales and crowding
levels. Code is available at
https://github.com/shizenglin/Counting-with-Focus-for-Free
