论文标题
加强本地特征代表，以进行弱监督的密集人群计数
Reinforcing Local Feature Representation for Weakly-Supervised Dense Crowd Counting
论文作者
论文摘要
由于大量的注释，完全监督的人群计数是一项费力的任务。很少有作品专注于每周监督的人群计数，在这里，只有全球人群数量可供培训。每周监督人群计数的主要挑战是缺乏地方监督信息。为了解决这个问题，我们提出了一个自适应特征相似性学习（SFSL）网络和全球本地一致性（GLC）损失，以增强局部特征表示。我们引入了一个特征向量，该功能向量代表了人的公正特征估计。该网络会自动自适应更新功能矢量，并利用特征相似性来回归人群。此外，拟议的GLC损失还利用了全球和本地区域的网络估计之间的一致性。实验结果表明，我们提出的基于不同骨架的方法缩小了弱监督和完全监督密集的人群计数之间的差距。
Fully-supervised crowd counting is a laborious task due to the large amounts of annotations. Few works focus on weekly-supervised crowd counting, where only the global crowd numbers are available for training. The main challenge of weekly-supervised crowd counting is the lack of local supervision information. To address this problem, we propose a self-adaptive feature similarity learning (SFSL) network and a global-local consistency (GLC) loss to reinforce local feature representation. We introduce a feature vector which represents the unbiased feature estimation of persons. The network updates the feature vector self-adaptively and utilizes the feature similarity for the regression of crowd numbers. Besides, the proposed GLC loss leverages the consistency between the network estimations from global and local areas. The experimental results demonstrate that our proposed method based on different backbones narrows the gap between weakly-supervised and fully-supervised dense crowd counting.
