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: https://github.com/cha15yq/MRC-Crowd.

翻译：为减轻训练可靠人群计数模型所需的繁重标注负担，从而通过利用更多数据使模型更具实用性和准确性，本文提出了一种基于均值教师框架的全新半监督方法。当可用标注数据稀缺时，模型容易过拟合局部区域。在此背景下，仅依靠未标注数据提升局部区域预测精度的传统方法效果有限。为此，我们提出了一种更精细化的策略：培养模型内在的"瞬数"能力。这种能力使模型能够通过理解人群场景来精确估计区域计数，模拟人类认知过程。为实现这一目标，我们对未标注数据施加掩码，引导模型基于整体线索对这些掩码区域进行预测。此外，为辅助特征学习，我们还引入了一项细粒度密度分类任务。由于本方法无严格的结构或损失约束，因此具有通用性，可适用于大多数现有的人群计数方法。进一步地，我们观察到采用本框架训练的模型展现出类似"瞬数"的行为：它能通过"一瞥"准确预测低密度区域，同时融合局部细节预测高密度区域。本方法达到了最先进的性能，在ShanghaiTech A和UCF-QNRF等挑战性基准测试上大幅超越先前方法。代码已开源：https://github.com/cha15yq/MRC-Crowd。