  Labeling is onerous for crowd counting as it should annotate each individual
in crowd images. Recently, several methods have been proposed for
semi-supervised crowd counting to reduce the labeling efforts. Given a limited
labeling budget, they typically select a few crowd images and densely label all
individuals in each of them. Despite the promising results, we argue the
None-or-All labeling strategy is suboptimal as the densely labeled individuals
in each crowd image usually appear similar while the massive unlabeled crowd
images may contain entirely diverse individuals. To this end, we propose to
break the labeling chain of previous methods and make the first attempt to
reduce spatial labeling redundancy for semi-supervised crowd counting. First,
instead of annotating all the regions in each crowd image, we propose to
annotate the representative ones only. We analyze the region representativeness
from both vertical and horizontal directions, and formulate them as cluster
centers of Gaussian Mixture Models. Additionally, to leverage the rich
unlabeled regions, we exploit the similarities among individuals in each crowd
image to directly supervise the unlabeled regions via feature propagation
instead of the error-prone label propagation employed in the previous methods.
In this way, we can transfer the original spatial labeling redundancy caused by
individual similarities to effective supervision signals on the unlabeled
regions. Extensive experiments on the widely-used benchmarks demonstrate that
our method can outperform previous best approaches by a large margin.
