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

The class-agnostic counting (CAC) task has recently been proposed to solve
the problem of counting all objects of an arbitrary class with several
exemplars given in the input image. To address this challenging task, existing
leading methods all resort to density map regression, which renders them
impractical for downstream tasks that require object locations and restricts
their ability to well explore the scale information of exemplars for
supervision. To address the limitations, we propose a novel localization-based
CAC approach, termed Scale-modulated Query and Localization Network (SQLNet).
It fully explores the scales of exemplars in both the query and localization
stages and achieves effective counting by accurately locating each object and
predicting its approximate size. Specifically, during the query stage, rich
discriminative representations of the target class are acquired by the
Hierarchical Exemplars Collaborative Enhancement (HECE) module from the few
exemplars through multi-scale exemplar cooperation with equifrequent size
prompt embedding. These representations are then fed into the Exemplars-Unified
Query Correlation (EUQC) module to interact with the query features in a
unified manner and produce the correlated query tensor. In the localization
stage, the Scale-aware Multi-head Localization (SAML) module utilizes the query
tensor to predict the confidence, location, and size of each potential object.
Moreover, a scale-aware localization loss is introduced, which exploits
flexible location associations and exemplar scales for supervision to optimize
the model performance. Extensive experiments demonstrate that SQLNet
outperforms state-of-the-art methods on popular CAC benchmarks, achieving
excellent performance not only in counting accuracy but also in localization
and bounding box generation. Our codes will be available at
this https URL

通过局部化基础的类别无关计数方法，本研究提出了一种新的基于尺度调节的查询和定位网络（SQLNet），可以准确定位并预测每个目标的大致尺寸，以实现有效的计数；SQLNet 在流行的类别无关计数基准任务上表现优异，不仅在计数准确度上优于最先进的方法，还在定位和边界框生成方面表现出色。