  Most existing crowd counting methods require object location-level
annotation, i.e., placing a dot at the center of an object. While being simpler
than the bounding-box or pixel-level annotation, obtaining this annotation is
still labor-intensive and time-consuming especially for images with highly
crowded scenes. On the other hand, weaker annotations that only know the total
count of objects can be almost effortless in many practical scenarios. Thus, it
is desirable to develop a learning method that can effectively train models
from count-level annotations. To this end, this paper studies the problem of
weakly-supervised crowd counting which learns a model from only a small amount
of location-level annotations (fully-supervised) but a large amount of
count-level annotations (weakly-supervised). To perform effective training in
this scenario, we observe that the direct solution of regressing the integral
of density map to the object count is not sufficient and it is beneficial to
introduce stronger regularizations on the predicted density map of
weakly-annotated images. We devise a simple-yet-effective training strategy,
namely Multiple Auxiliary Tasks Training (MATT), to construct regularizes for
restricting the freedom of the generated density maps. Through extensive
experiments on existing datasets and a newly proposed dataset, we validate the
effectiveness of the proposed weakly-supervised method and demonstrate its
superior performance over existing solutions.
