  Single image-based crowd counting has recently witnessed increased focus, but
many leading methods are far from optimal, especially in highly congested
scenes. In this paper, we present Hierarchical Attention-based Crowd Counting
Network (HA-CCN) that employs attention mechanisms at various levels to
selectively enhance the features of the network. The proposed method, which is
based on the VGG16 network, consists of a spatial attention module (SAM) and a
set of global attention modules (GAM). SAM enhances low-level features in the
network by infusing spatial segmentation information, whereas the GAM focuses
on enhancing channel-wise information in the higher level layers. The proposed
method is a single-step training framework, simple to implement and achieves
state-of-the-art results on different datasets.
  Furthermore, we extend the proposed counting network by introducing a novel
set-up to adapt the network to different scenes and datasets via weak
supervision using image-level labels. This new set up reduces the burden of
acquiring labour intensive point-wise annotations for new datasets while
improving the cross-dataset performance.
