Multidomain crowd counting aims to learn a general model for multiple diverse datasets. However, deep networks prefer modeling distributions of the dominant domains instead of all domains, which is known as domain bias. In this study, we propose a simple-yet-effective Modulating Domain-specific Knowledge Network (MDKNet) to handle the domain bias issue in multidomain crowd counting. MDKNet is achieved by employing the idea of `modulating', enabling deep network balancing and modeling different distributions of diverse datasets with little bias. Specifically, we propose an Instance-specific Batch Normalization (IsBN) module, which serves as a base modulator to refine the information flow to be adaptive to domain distributions. To precisely modulating the domain-specific information, the Domain-guided Virtual Classifier (DVC) is then introduced to learn a domain-separable latent space. This space is employed as an input guidance for the IsBN modulator, such that the mixture distributions of multiple datasets can be well treated. Extensive experiments performed on popular benchmarks, including Shanghai-tech A/B, QNRF and NWPU, validate the superiority of MDKNet in tackling multidomain crowd counting and the effectiveness for multidomain learning. Code is available at \url{https://github.com/csguomy/MDKNet}.

翻译：多域人群计数旨在为多个不同数据集学习一个通用模型。然而，深度网络更倾向于建模主导域而非所有域的分布，这被称为域偏差。本研究提出一种简单而有效的领域特定知识调节网络（MDKNet）来处理多域人群计数中的域偏差问题。MDKNet通过采用“调节”理念实现，使深度网络能够均衡并建模不同数据集的分布，且偏差极小。具体而言，我们提出实例特定批归一化（IsBN）模块，作为基础调节器优化信息流，使其适应域分布。为精确调节领域特定信息，进一步引入域引导虚拟分类器（DVC）以学习域可分离的隐空间。该空间作为IsBN调节器的输入引导，从而有效处理多数据集的混合分布。在包括Shanghai-tech A/B、QNRF和NWPU在内的主流基准数据集上的大量实验验证了MDKNet在处理多域人群计数方面的优越性及其在多域学习中的有效性。代码见\url{https://github.com/csguomy/MDKNet}。