Crowd counting is an important yet challenging task due to the large scale
and density variation. Recent investigations have shown that distilling rich
relations among multi-scale features and exploiting useful information from the
auxiliary task, i.e., localization, are vital for this task. Nevertheless, how
to comprehensively leverage these relations within a unified network
architecture is still a challenging problem. In this paper, we present a novel
network structure called Hybrid Graph Neural Network (HyGnn) which targets to
relieve the problem by interweaving the multi-scale features for crowd density
as well as its auxiliary task (localization) together and performing joint
reasoning over a graph. Specifically, HyGnn integrates a hybrid graph to
jointly represent the task-specific feature maps of different scales as nodes,
and two types of relations as edges:(i) multi-scale relations for capturing the
feature dependencies across scales and (ii) mutual beneficial relations
building bridges for the cooperation between counting and localization. Thus,
through message passing, HyGnn can distill rich relations between the nodes to
obtain more powerful representations, leading to robust and accurate results.
Our HyGnn performs significantly well on four challenging datasets:
ShanghaiTech Part A, ShanghaiTech Part B, UCF_CC_50 and UCF_QNRF, outperforming
the state-of-the-art approaches by a large margin.

本文提出了一种新的网络结构 —— 混合图神经网络（HyGnn），通过交织多尺度特征以及辅助任务（定位）来执行图推理，从而缓解了人群密度估计中的挑战问题，实现了比现有方法更为准确的效果。