Semi-supervised crowd counting is an important yet challenging task. A popular approach is to iteratively generate pseudo-labels for unlabeled data and add them to the training set. The key is to use uncertainty to select reliable pseudo-labels. In this paper, we propose a novel method to calibrate model uncertainty for crowd counting. Our method takes a supervised uncertainty estimation strategy to train the model through a surrogate function. This ensures the uncertainty is well controlled throughout the training. We propose a matching-based patch-wise surrogate function to better approximate uncertainty for crowd counting tasks. The proposed method pays a sufficient amount of attention to details, while maintaining a proper granularity. Altogether our method is able to generate reliable uncertainty estimation, high quality pseudolabels, and achieve state-of-the-art performance in semisupervised crowd counting.

半监督式人群计数中，通过使用伪标签和不确定性选择可靠的伪标签来训练模型的一种新方法，该方法通过匹配的基于补丁的代理函数更好地近似了人群计数任务的不确定性，从而生成了可靠的不确定性估计、高质量的伪标签，并达到了最新技术水平。

半监督式人群计数的不确定性校准