Recent deep networks have convincingly demonstrated high capability in crowd counting, which is a critical task attracting widespread attention due to its various industrial applications. Despite such progress, trained data-dependent models usually can not generalize well to unseen scenarios because of the inherent domain shift. To facilitate this issue, this paper proposes a novel adversarial scoring network (ASNet) to gradually bridge the gap across domains from coarse to fine granularity. In specific, at the coarse-grained stage, we design a dual-discriminator strategy to adapt source domain to be close to the targets from the perspectives of both global and local feature space via adversarial learning. The distributions between two domains can thus be aligned roughly. At the fine-grained stage, we explore the transferability of source characteristics by scoring how similar the source samples are to target ones from multiple levels based on generative probability derived from coarse stage. Guided by these hierarchical scores, the transferable source features are properly selected to enhance the knowledge transfer during the adaptation process. With the coarse-to-fine design, the generalization bottleneck induced from the domain discrepancy can be effectively alleviated. Three sets of migration experiments show that the proposed methods achieve state-of-the-art counting performance compared with major unsupervised methods.

中文翻译：

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从粗到细：通过对抗性评分网络进行域自适应人群计数

最近的深度网络令人信服地展示了人群计数的高能力，这是一项因其各种工业应用而引起广泛关注的关键任务。尽管取得了这样的进展，但由于固有的领域转移，训练有素的依赖于数据的模型通常不能很好地推广到看不见的场景。为了解决这个问题，本文提出了一种新颖的对抗性评分网络 (ASNet)，以逐步弥合域之间从粗粒度到细粒度的差距。具体来说，在粗粒度阶段，我们设计了一种双鉴别器策略，通过对抗性学习，从全局和局部特征空间的角度使源域接近目标。两个域之间的分布因此可以大致对齐。在细粒度阶段，我们通过基于从粗略阶段得出的生成概率对源样本与来自多个级别的目标样本的相似程度进行评分来探索源特征的可转移性。由这些分层分数引导，正确选择可转移的源特征，以增强适应过程中的知识传输。通过从粗到细的设计，可以有效缓解由域差异引起的泛化瓶颈。三组迁移实验表明，与主要的无监督方法相比，所提出的方法实现了最先进的计数性能。正确选择可转移的源特征以增强适应过程中的知识传输。通过从粗到细的设计，可以有效缓解由域差异引起的泛化瓶颈。三组迁移实验表明，与主要的无监督方法相比，所提出的方法实现了最先进的计数性能。正确选择可转移的源特征以增强适应过程中的知识传输。通过从粗到细的设计，可以有效缓解由域差异引起的泛化瓶颈。三组迁移实验表明，与主要的无监督方法相比，所提出的方法实现了最先进的计数性能。