  Crowd image is arguably one of the most laborious data to annotate. In this
paper, we devote to reduce the massive demand of densely labeled crowd data,
and propose a novel weakly-supervised setting, in which we leverage the binary
ranking of two images with high-contrast crowd counts as training guidance. To
enable training under this new setting, we convert the crowd count regression
problem to a ranking potential prediction problem. In particular, we tailor a
Siamese Ranking Network that predicts the potential scores of two images
indicating the ordering of the counts. Hence, the ultimate goal is to assign
appropriate potentials for all the crowd images to ensure their orderings obey
the ranking labels. On the other hand, potentials reveal the relative crowd
sizes but cannot yield an exact crowd count. We resolve this problem by
introducing "anchors" during the inference stage. Concretely, anchors are a few
images with count labels used for referencing the corresponding counts from
potential scores by a simple linear mapping function. We conduct extensive
experiments to study various combinations of supervision, and we show that the
proposed method outperforms existing weakly-supervised methods without
additional labeling effort by a large margin.
