In this paper, we propose a new agency-guided semi-supervised counting approach. First, we build a learnable auxiliary structure, namely the density agency to bring the recognized foreground regional features close to corresponding density sub-classes (agents) and push away background ones. Second, we propose a density-guided contrastive learning loss to consolidate the backbone feature extractor. Third, we build a regression head by using a transformer structure to refine the foreground features further. Finally, an efficient noise depression loss is provided to minimize the negative influence of annotation noises. Extensive experiments on four challenging crowd counting datasets demonstrate that our method achieves superior performance to the state-of-the-art semi-supervised counting methods by a large margin. Code is available.

该研究提出了一种新的半监督计数方法，使用可学习的密度机构来将已识别的前景区域特征与其对应的密度子类（代理）接近，并将背景特征推开。通过密度导向的对比损失巩固骨干特征提取器，再使用变换器结构构建回归头进一步优化前景特征。提供了高效的噪声压制损失以最小化注释噪声的负面影响。在四个具有挑战性的人群计数数据集上的广泛实验表明，我们的方法比最先进的半监督计数方法具有更优越的性能。

密度代理半监督群众计数