To alleviate the heavy annotation burden for training a reliable crowd
counting model and thus make the model more practicable and accurate by being
able to benefit from more data, this paper presents a new semi-supervised
method based on the mean teacher framework. When there is a scarcity of labeled
data available, the model is prone to overfit local patches. Within such
contexts, the conventional approach of solely improving the accuracy of local
patch predictions through unlabeled data proves inadequate. Consequently, we
propose a more nuanced approach: fostering the model's intrinsic 'subitizing'
capability. This ability allows the model to accurately estimate the count in
regions by leveraging its understanding of the crowd scenes, mirroring the
human cognitive process. To achieve this goal, we apply masking on unlabeled
data, guiding the model to make predictions for these masked patches based on
the holistic cues. Furthermore, to help with feature learning, herein we
incorporate a fine-grained density classification task. Our method is general
and applicable to most existing crowd counting methods as it doesn't have
strict structural or loss constraints. In addition, we observe that the model
trained with our framework exhibits a 'subitizing'-like behavior. It accurately
predicts low-density regions with only a 'glance', while incorporating local
details to predict high-density regions. Our method achieves the
state-of-the-art performance, surpassing previous approaches by a large margin
on challenging benchmarks such as ShanghaiTech A and UCF-QNRF. The code is
available at: this https URL

该研究提出了一种新的半监督方法，基于平均教师框架，以减轻训练可靠的人群计数模型所需的大量标注负担，从而增加数据量，提高模型的实用性和准确性。经验证明，在标签数据稀缺的情况下，仅通过未标记的数据来提高局部区域预测的准确性是不充分的，因此需要采用更细粒度的方法培养模型的内在 “突显” 能力，以 accurately estimate the count in regions by leveraging its understanding of the crowd scenes，并结合局部细节预测高密度区域，该方法在人群计数领域取得了最先进的表现。