This paper focuses on semi-supervised crowd counting, where only a small
portion of the training data are labeled. We formulate the pixel-wise density
value to regress as a probability distribution, instead of a single
deterministic value. On this basis, we propose a semi-supervised crowd-counting
model. Firstly, we design a pixel-wise distribution matching loss to measure
the differences in the pixel-wise density distributions between the prediction
and the ground truth; Secondly, we enhance the transformer decoder by using
density tokens to specialize the forwards of decoders w.r.t. different density
intervals; Thirdly, we design the interleaving consistency self-supervised
learning mechanism to learn from unlabeled data efficiently. Extensive
experiments on four datasets are performed to show that our method clearly
outperforms the competitors by a large margin under various labeled ratio
settings. Code will be released at
this https URL

本文提出了一种半监督人群计数模型，通过将像素密度值建模为概率分布，设计了像素密度分布匹配损失、增强变压器解码器以及交错一致性自监督学习机制，并在四个数据集上进行了广泛实验，结果表明该方法明显优于竞争模型。

基于逐像素密度分布建模的半监督计数

Semi-supervised Counting via Pixel-by-pixel Density Distribution  Modelling

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。