Current image-based crowd counting widely employs density map regression due
to its promising results. However, the method often suffers from severe
performance degradation when tested on data from unseen scenarios. To address
this so-called "domain shift" problem, we investigate single domain
generalization (SDG) for crowd counting. The existing SDG approaches are mainly
for 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 SDG approach effective even for
narrow source distribution. Reconstructing diverse features for density map
regression with a single memory bank, MPCount retains only domain-invariant
representations using a content error mask and attention consistency loss. It
further introduces patch-wise classification as an auxiliary task to boost the
robustness of density prediction to achieve highly accurate labels. 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 of narrow source distribution. Code
is available at this https URL

通过使用单一内存库进行特征重构、多样性密度图回归和辅助任务分类，提出了一种名为 MPCount 的单一领域泛化方法，有效地解决了图像中的人群计数问题中的域偏移问题，并在不同数据集上实验证明其在狭窄源分布的训练数据中的非常准确的标签上优于现有方法的计数准确性。