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。

人群计数的单一领域泛化

Single Domain Generalization for Crowd Counting

We present a method for image-based crowd counting, one that can predict a
crowd density map together with the uncertainty values pertaining to the
predicted density map. To obtain prediction uncertainty, we model the crowd
density values using Gaussian distributions and develop a convolutional neural
network architecture to predict these distributions. A key advantage of our
method over existing crowd counting methods is its ability to quantify the
uncertainty of its predictions. We illustrate the benefits of knowing the
prediction uncertainty by developing a method to reduce the human annotation
effort needed to adapt counting networks to a new domain. We present sample
selection strategies which make use of the density and uncertainty of
predictions from the networks trained on one domain to select the informative
images from a target domain of interest to acquire human annotation. We show
that our sample selection strategy drastically reduces the amount of labeled
data from the target domain needed to adapt a counting network trained on a
source domain to the target domain. Empirically, the networks trained on
UCF-QNRF dataset can be adapted to surpass the performance of the previous
state-of-the-art results on NWPU dataset and Shanghaitech dataset using only
17$\%$ of the labeled training samples from the target domain.

本文提出了一种基于图像的人群计数方法，可以预测人群密度地图以及与预测密度地图相关的不确定性值，并开发出卷积神经网络架构来预测这些分布，通过使用高斯分布对人群密度值进行建模来获得预测不确定性，并开发了样本选择策略以减少适应计数网络所需的人工注释工作量。