This paper presents a new annotation method called Sparse Annotation (SA) for crowd counting, which reduces human labeling efforts by sparsely labeling individuals in an image. We argue that sparse labeling can reduce the redundancy of full annotation and capture more diverse information from distant individuals that is not fully captured by Partial Annotation methods. Besides, we propose a point-based Progressive Point Matching network (PPM) to better explore the crowd from the whole image with sparse annotation, which includes a Proposal Matching Network (PMN) and a Performance Restoration Network (PRN). The PMN generates pseudo-point samples using a basic point classifier, while the PRN refines the point classifier with the pseudo points to maximize performance. Our experimental results show that PPM outperforms previous semi-supervised crowd counting methods with the same amount of annotation by a large margin and achieves competitive performance with state-of-the-art fully-supervised methods.

本文介绍了一种名为Sparse Annotation（SA）的新的众包计数注释方法，我们提出了一种基于点的渐进点匹配网络（PPM），通过Sparse Annotation更好地探索群众；实验结果表明，在注释数量相同的情况下，PPM优于以前的半监督计数方法，并与最先进的全监督方法具有竞争性的性能。

稀疏标注下的众包计数