  Recently, crowd counting using supervised learning achieves a remarkable
improvement. Nevertheless, most counters rely on a large amount of manually
labeled data. With the release of synthetic crowd data, a potential alternative
is transferring knowledge from them to real data without any manual label.
However, there is no method to effectively suppress domain gaps and output
elaborate density maps during the transferring. To remedy the above problems,
this paper proposes a Domain-Adaptive Crowd Counting (DACC) framework, which
consists of a high-quality image translation and density map reconstruction. To
be specific, the former focuses on translating synthetic data to realistic
images, which prompts the translation quality by segregating
domain-shared/independent features and designing content-aware consistency
loss. The latter aims at generating pseudo labels on real scenes to improve the
prediction quality. Next, we retrain a final counter using these pseudo labels.
Adaptation experiments on six real-world datasets demonstrate that the proposed
method outperforms the state-of-the-art methods.
