  In crowd counting datasets, people appear at different scales, depending on
their distance from the camera. To address this issue, we propose a novel
multi-branch scale-aware attention network that exploits the hierarchical
structure of convolutional neural networks and generates, in a single forward
pass, multi-scale density predictions from different layers of the
architecture. To aggregate these maps into our final prediction, we present a
new soft attention mechanism that learns a set of gating masks. Furthermore, we
introduce a scale-aware loss function to regularize the training of different
branches and guide them to specialize on a particular scale. As this new
training requires annotations for the size of each head, we also propose a
simple, yet effective technique to estimate them automatically. Finally, we
present an ablation study on each of these components and compare our approach
against the literature on 4 crowd counting datasets: UCF-QNRF, ShanghaiTech A &
B and UCF_CC_50. Our approach achieves state-of-the-art on all them with a
remarkable improvement on UCF-QNRF (+25% reduction in error).
