  Most conventional crowd counting methods utilize a fully-supervised learning
framework to establish a mapping between scene images and crowd density maps.
They usually rely on a large quantity of costly and time-intensive pixel-level
annotations for training supervision. One way to mitigate the intensive
labeling effort and improve counting accuracy is to leverage large amounts of
unlabeled images. This is attributed to the inherent self-structural
information and rank consistency within a single image, offering additional
qualitative relation supervision during training. Contrary to earlier methods
that utilized the rank relations at the original image level, we explore such
rank-consistency relation within the latent feature spaces. This approach
enables the incorporation of numerous pyramid partial orders, strengthening the
model representation capability. A notable advantage is that it can also
increase the utilization ratio of unlabeled samples. Specifically, we propose a
Deep Rank-consistEnt pyrAmid Model (DREAM), which makes full use of rank
consistency across coarse-to-fine pyramid features in latent spaces for
enhanced crowd counting with massive unlabeled images. In addition, we have
collected a new unlabeled crowd counting dataset, FUDAN-UCC, comprising 4,000
images for training purposes. Extensive experiments on four benchmark datasets,
namely UCF-QNRF, ShanghaiTech PartA and PartB, and UCF-CC-50, show the
effectiveness of our method compared with previous semi-supervised methods. The
codes are available at https://github.com/bridgeqiqi/DREAM.
