  Recent crowd counting approaches have achieved excellent performance.
However, they are essentially based on fully supervised paradigm and require
large number of annotated samples. Obtaining annotations is an expensive and
labour-intensive process. In this work, we focus on reducing the annotation
efforts by learning to count in the crowd from limited number of labeled
samples while leveraging a large pool of unlabeled data. Specifically, we
propose a Gaussian Process-based iterative learning mechanism that involves
estimation of pseudo-ground truth for the unlabeled data, which is then used as
supervision for training the network. The proposed method is shown to be
effective under the reduced data (semi-supervised) settings for several
datasets like ShanghaiTech, UCF-QNRF, WorldExpo, UCSD, etc. Furthermore, we
demonstrate that the proposed method can be leveraged to enable the network in
learning to count from synthetic dataset while being able to generalize better
to real-world datasets (synthetic-to-real transfer).
