论文标题
Adacrowd：未标记的场景改编以供人群计数
AdaCrowd: Unlabeled Scene Adaptation for Crowd Counting
论文作者
论文摘要
我们解决了基于图像的人群计数问题。特别是，我们提出了一个新问题，称为未标记的场景自适应人群计数。给定一个新的目标场景，我们希望根据目标数据专门针对此特定场景的人群计数模型，以捕获有关新场景的一些信息。在本文中，我们建议使用目标场景中的一个或多个未标记的图像进行适应。与现有的问题设置（例如完全监督）相比，我们提出的问题设置更接近人群计数系统的现实应用程序。我们介绍了一个新颖的Adacrowd框架来解决这个问题。我们的框架包括人群计数网络和一个指导网络。指南网络基于特定场景中未标记的图像来预测人群计数网络中的某些参数。这使我们的模型可以适应不同的目标场景。与其他替代方法相比，几个具有挑战性的基准数据集的实验结果证明了我们提出的方法的有效性。代码可从https://github.com/maheshkkumar/adacrowd获得。
We address the problem of image-based crowd counting. In particular, we propose a new problem called unlabeled scene-adaptive crowd counting. Given a new target scene, we would like to have a crowd counting model specifically adapted to this particular scene based on the target data that capture some information about the new scene. In this paper, we propose to use one or more unlabeled images from the target scene to perform the adaptation. In comparison with the existing problem setups (e.g. fully supervised), our proposed problem setup is closer to the real-world applications of crowd counting systems. We introduce a novel AdaCrowd framework to solve this problem. Our framework consists of a crowd counting network and a guiding network. The guiding network predicts some parameters in the crowd counting network based on the unlabeled images from a particular scene. This allows our model to adapt to different target scenes. The experimental results on several challenging benchmark datasets demonstrate the effectiveness of our proposed approach compared with other alternative methods. Code is available at https://github.com/maheshkkumar/adacrowd.
