  Labeled crowd scene images are expensive and scarce. To significantly reduce
the requirement of the labeled images, we propose ColorCount, a novel CNN-based
approach by combining self-supervised transfer colorization learning and global
prior classification to leverage the abundantly available unlabeled data. The
self-supervised colorization branch learns the semantics and surface texture of
the image by using its color components as pseudo labels. The classification
branch extracts global group priors by learning correlations among image
clusters. Their fused resultant discriminative features (global priors,
semantics and textures) provide ample priors for counting, hence significantly
reducing the requirement of labeled images. We conduct extensive experiments on
four challenging benchmarks. ColorCount achieves much better performance as
compared with other unsupervised approaches. Its performance is close to the
supervised baseline with substantially less labeled data (10\% of the original
one).
