  Semi-supervised approaches for crowd counting attract attention, as the fully
supervised paradigm is expensive and laborious due to its request for a large
number of images of dense crowd scenarios and their annotations. This paper
proposes a spatial uncertainty-aware semi-supervised approach via regularized
surrogate task (binary segmentation) for crowd counting problems. Different
from existing semi-supervised learning-based crowd counting methods, to exploit
the unlabeled data, our proposed spatial uncertainty-aware teacher-student
framework focuses on high confident regions' information while addressing the
noisy supervision from the unlabeled data in an end-to-end manner.
Specifically, we estimate the spatial uncertainty maps from the teacher model's
surrogate task to guide the feature learning of the main task (density
regression) and the surrogate task of the student model at the same time.
Besides, we introduce a simple yet effective differential transformation layer
to enforce the inherent spatial consistency regularization between the main
task and the surrogate task in the student model, which helps the surrogate
task to yield more reliable predictions and generates high-quality uncertainty
maps. Thus, our model can also address the task-level perturbation problems
that occur spatial inconsistency between the primary and surrogate tasks in the
student model. Experimental results on four challenging crowd counting datasets
demonstrate that our method achieves superior performance to the
state-of-the-art semi-supervised methods.
