Calibrating Uncertainty for Semi-Supervised Crowd Counting
Chen Li Xiaoling Hu Shahira Abousamra Chao Chen
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.