Semi-supervised approaches for crowd counting attract attention, as the fully supervised paradigm is expensive and laborious due to its request for a large number of images of dense crowd scenarios and their annotations. This paper proposes a spatial uncertainty-aware semi-supervised approach via regularized surrogate task (binary segmentation) for crowd counting problems. Different from existing semi-supervised learning-based crowd counting methods, to exploit the unlabeled data, our proposed spatial uncertainty-aware teacher-student framework focuses on high confident regions' information while addressing the noisy supervision from the unlabeled data in an end-to-end manner. Specifically, we estimate the spatial uncertainty maps from the teacher model's surrogate task to guide the feature learning of the main task (density regression) and the surrogate task of the student model at the same time. Besides, we introduce a simple yet effective differential transformation layer to enforce the inherent spatial consistency regularization between the main task and the surrogate task in the student model, which helps the surrogate task to yield more reliable predictions and generates high-quality uncertainty maps. Thus, our model can also address the task-level perturbation problems that occur spatial inconsistency between the primary and surrogate tasks in the student model. Experimental results on four challenging crowd counting datasets demonstrate that our method achieves superior performance to the state-of-the-art semi-supervised methods.
翻译:由于完全监督的范式要求大量显示密集人群情景及其说明的图像,充分监督的人群计数的半监督方法十分昂贵和艰巨,因为它要求大量显示大量密集人群情景及其说明。本文件建议通过固定化替代任务(双分制),对人群计数问题采取空间不确定性半监督方法。不同于现有的半监督的基于学习的人群计数方法,以利用未加标签的数据,我们拟议的空间不确定性教师-学生框架侧重于高度自信区域的信息,同时以端对端方式处理来自未贴标签数据的大量监管的噪音。具体地说,我们根据教师模型的代孕任务对空间不确定性地图进行估算,以指导主要任务(密度回归)的特点学习和学生模型的代孕任务。此外,我们引入了一个简单而有效的差异转换层,以实施主要任务与学生模型的代孕期任务之间的内在空间一致性调整,从而帮助代孕任务产生更可靠的预测,并生成高质量的不确定性图。因此,我们根据教师模型的代孕期任务对空间不确定性进行空间不确定性地图的地图进行估算,因此,我们的模式还可以在学生的四级级实验方法上显示我们的任务等级的计算结果。