Cross-domain crowd counting (CDCC) is a hot topic due to its importance in public safety. The purpose of CDCC is to alleviate the domain shift between the source and target domain. Recently, typical methods attempt to extract domain-invariant features via image translation and adversarial learning. When it comes to specific tasks, we find that the domain shifts are reflected on model parameters' differences. To describe the domain gap directly at the parameter-level, we propose a Neuron Linear Transformation (NLT) method, exploiting domain factor and bias weights to learn the domain shift. Specifically, for a specific neuron of a source model, NLT exploits few labeled target data to learn domain shift parameters. Finally, the target neuron is generated via a linear transformation. Extensive experiments and analysis on six real-world datasets validate that NLT achieves top performance compared with other domain adaptation methods. An ablation study also shows that the NLT is robust and more effective than supervised and fine-tune training. Code is available at: \url{https://github.com/taohan10200/NLT}.

翻译：由于在公共安全方面的重要性, CDCCC 的目的是缓解源与目标域之间的域变。 最近, 典型的方法试图通过图像翻译和对抗性学习来提取域变量特征。 当涉及到具体任务时, 我们发现域变换反映了模型参数的差异。 为了直接描述参数一级的域差, 我们提议采用Neron线性变换( NLT) 方法, 利用域因子和偏差权重来学习域变换。 具体来说, NLT 利用了少数标注的目标数据来学习域变换参数。 最后, 目标神经元是通过线性变换生成的。 对六个真实世界数据集的广泛实验和分析证实, NLT 与其他域适应方法相比, 取得顶级性成绩。 一项通货膨胀研究还显示, NLT 比监管和微调培训更有力、更有效。 代码见:\ url{https://github.comtohan10200/NLT}