  Fully-supervised crowd counting is a laborious task due to the large amounts
of annotations. Few works focus on weekly-supervised crowd counting, where only
the global crowd numbers are available for training. The main challenge of
weekly-supervised crowd counting is the lack of local supervision information.
To address this problem, we propose a self-adaptive feature similarity learning
(SFSL) network and a global-local consistency (GLC) loss to reinforce local
feature representation. We introduce a feature vector which represents the
unbiased feature estimation of persons. The network updates the feature vector
self-adaptively and utilizes the feature similarity for the regression of crowd
numbers. Besides, the proposed GLC loss leverages the consistency between the
network estimations from global and local areas. The experimental results
demonstrate that our proposed method based on different backbones narrows the
gap between weakly-supervised and fully-supervised dense crowd counting.
