  State-of-the-art methods for counting people in crowded scenes rely on deep
networks to estimate crowd density. While effective, these data-driven
approaches rely on large amount of data annotation to achieve good performance,
which stops these models from being deployed in emergencies during which data
annotation is either too costly or cannot be obtained fast enough.
  One popular solution is to use synthetic data for training. Unfortunately,
due to domain shift, the resulting models generalize poorly on real imagery. We
remedy this shortcoming by training with both synthetic images, along with
their associated labels, and unlabeled real images. To this end, we force our
network to learn perspective-aware features by training it to recognize
upside-down real images from regular ones and incorporate into it the ability
to predict its own uncertainty so that it can generate useful pseudo labels for
fine-tuning purposes. This yields an algorithm that consistently outperforms
state-of-the-art cross-domain crowd counting ones without any extra computation
at inference time.
