  In this paper, we propose a new agency-guided semi-supervised counting
approach. First, we build a learnable auxiliary structure, namely the density
agency to bring the recognized foreground regional features close to
corresponding density sub-classes (agents) and push away background ones.
Second, we propose a density-guided contrastive learning loss to consolidate
the backbone feature extractor. Third, we build a regression head by using a
transformer structure to refine the foreground features further. Finally, an
efficient noise depression loss is provided to minimize the negative influence
of annotation noises. Extensive experiments on four challenging crowd counting
datasets demonstrate that our method achieves superior performance to the
state-of-the-art semi-supervised counting methods by a large margin. Code is
available.
