  Domain shift across crowd data severely hinders crowd counting models to
generalize to unseen scenarios. Although domain adaptive crowd counting
approaches close this gap to a certain extent, they are still dependent on the
target domain data to adapt (e.g. finetune) their models to the specific
domain. In this paper, we aim to train a model based on a single source domain
which can generalize well on any unseen domain. This falls into the realm of
domain generalization that remains unexplored in crowd counting. We first
introduce a dynamic sub-domain division scheme which divides the source domain
into multiple sub-domains such that we can initiate a meta-learning framework
for domain generalization. The sub-domain division is dynamically refined
during the meta-learning. Next, in order to disentangle domain-invariant
information from domain-specific information in image features, we design the
domain-invariant and -specific crowd memory modules to re-encode image
features. Two types of losses, i.e. feature reconstruction and orthogonal
losses, are devised to enable this disentanglement. Extensive experiments on
several standard crowd counting benchmarks i.e. SHA, SHB, QNRF, and NWPU, show
the strong generalizability of our method.
