论文标题
专注于语义一致性，以了解跨域的人群理解
Focus on Semantic Consistency for Cross-domain Crowd Understanding
论文作者
论文摘要
对于像素级人群的理解，它在数据收集和注释方面既耗时又费力。一些域适应算法试图通过使用合成数据的培训模型来解放它，而最近一些工作的结果证明了可行性。但是，我们发现背景区域中有大量估计错误阻碍了现有方法的性能。在本文中，我们提出了一种消除域的适应方法。根据语义一致性，在Deep Loese的合成和现实世界人群区域的特征中，我们首先引入语义提取器，以有效地区分高级语义信息的人群和背景。此外，为了进一步增强改编的模型，我们采用对抗性学习来使语义空间中的特征保持一致。三个代表性实际数据集的实验表明，所提出的域适应方案实现了跨域计数问题的最先进。
For pixel-level crowd understanding, it is time-consuming and laborious in data collection and annotation. Some domain adaptation algorithms try to liberate it by training models with synthetic data, and the results in some recent works have proved the feasibility. However, we found that a mass of estimation errors in the background areas impede the performance of the existing methods. In this paper, we propose a domain adaptation method to eliminate it. According to the semantic consistency, a similar distribution in deep layer's features of the synthetic and real-world crowd area, we first introduce a semantic extractor to effectively distinguish crowd and background in high-level semantic information. Besides, to further enhance the adapted model, we adopt adversarial learning to align features in the semantic space. Experiments on three representative real datasets show that the proposed domain adaptation scheme achieves the state-of-the-art for cross-domain counting problems.
