  Due to its promising results, density map regression has been widely employed
for image-based crowd counting. The approach, however, often suffers from
severe performance degradation when tested on data from unseen scenarios, the
so-called "domain shift" problem. To address the problem, we investigate in
this work single domain generalization (SDG) for crowd counting. The existing
SDG approaches are mainly for image classification and segmentation, and can
hardly be extended to our case due to its regression nature and label ambiguity
(i.e., ambiguous pixel-level ground truths). We propose MPCount, a novel
effective SDG approach even for narrow source distribution. MPCount stores
diverse density values for density map regression and reconstructs
domain-invariant features by means of only one memory bank, a content error
mask and attention consistency loss. By partitioning the image into grids, it
employs patch-wise classification as an auxiliary task to mitigate label
ambiguity. Through extensive experiments on different datasets, MPCount is
shown to significantly improve counting accuracy compared to the state of the
art under diverse scenarios unobserved in the training data characterized by
narrow source distribution. Code is available at
https://github.com/Shimmer93/MPCount.
