Domain adaptation is commonly employed in crowd counting to bridge the domain gaps between different datasets. However, existing domain adaptation methods tend to focus on inter-dataset differences while overlooking the intra-differences within the same dataset, leading to additional learning ambiguities. These domain-agnostic factors, e.g., density, surveillance perspective, and scale, can cause significant in-domain variations, and the misalignment of these factors across domains can lead to a drop in performance in cross-domain crowd counting. To address this issue, we propose a Domain-agnostically Aligned Optimal Transport (DAOT) strategy that aligns domain-agnostic factors between domains. The DAOT consists of three steps. First, individual-level differences in domain-agnostic factors are measured using structural similarity (SSIM). Second, the optimal transfer (OT) strategy is employed to smooth out these differences and find the optimal domain-to-domain misalignment, with outlier individuals removed via a virtual "dustbin" column. Third, knowledge is transferred based on the aligned domain-agnostic factors, and the model is retrained for domain adaptation to bridge the gap across domains. We conduct extensive experiments on five standard crowd-counting benchmarks and demonstrate that the proposed method has strong generalizability across diverse datasets. Our code will be available at: https://github.com/HopooLinZ/DAOT/.

翻译：领域自适应在人群计数中常被用于弥合不同数据集之间的领域差异。然而，现有领域自适应方法倾向于关注数据集间的差异，而忽视同一数据集内部的差异，导致额外的学习歧义。这些领域无关因素（如密度、监控视角和尺度）可能引起显著的域内变化，且这些因素在跨域间的错位会导致跨域人群计数性能下降。为解决该问题，我们提出一种领域无关对齐最优传输（DAOT）策略，用于对齐跨域间的领域无关因素。DAOT包含三个步骤：首先，利用结构相似性（SSIM）测量个体在领域无关因素上的差异；其次，采用最优传输（OT）策略平滑这些差异并寻找最优的域间错位，同时通过虚拟“垃圾桶”列剔除异常个体；最后，基于对齐后的领域无关因素进行知识迁移，并重新训练模型以实现领域自适应，从而弥合跨域差异。我们在五个标准人群计数基准上进行了大量实验，结果表明所提方法在不同数据集上具有强泛化性。代码链接：https://github.com/HopooLinZ/DAOT/。