cluster analysis has become one of the most exercised research areas over the
past few decades in computer science. As a consequence, numerous clustering
algorithms have already been developed to find appropriate partitions of a set
of objects. Given multiple such clustering solutions,
提出了一种新颖的群体计数方法，利用了学习排名框架中丰富可用的未标记人群图像。该方法采用裁剪图像的排名方式，通过考虑拥挤场景图像的任何子图像都可以保证包含相同或更少数量的人而解决了现有数据集规模有限的问题。本文还从 Google 收集了两个人群场景数据集，并演示了如何在多任务网络中合并学习排名和人群密度估计。在两个最具挑战性的群体计数数据集上的实验证明了该方法获得了最先进的结果。