  Dense crowd counting is a challenging task that demands millions of head
annotations for training models. Though existing self-supervised approaches
could learn good representations, they require some labeled data to map these
features to the end task of density estimation. We mitigate this issue with the
proposed paradigm of complete self-supervision, which does not need even a
single labeled image. The only input required to train, apart from a large set
of unlabeled crowd images, is the approximate upper limit of the crowd count
for the given dataset. Our method dwells on the idea that natural crowds follow
a power law distribution, which could be leveraged to yield error signals for
backpropagation. A density regressor is first pretrained with self-supervision
and then the distribution of predictions is matched to the prior by optimizing
Sinkhorn distance between the two. Experiments show that this results in
effective learning of crowd features and delivers significant counting
performance. Furthermore, we establish the superiority of our method in less
data setting as well. The code and models for our approach is available at
https://github.com/val-iisc/css-ccnn.
