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.

翻译：本文提出了一种名为稀疏标注（SA）的新型标注方法，用于人群计数，通过在图像中对个体进行稀疏标注来减少人类标注工作量。我们认为稀疏标注可以减少完全注释的冗余，并捕获远处个体的更多多样化信息，而这些信息不完全被部分标注方法捕获。此外，我们提出了一种基于点的逐步点匹配网络（PPM），用于更好地利用稀疏标注从整个图像中探索人群，其中包括建议匹配网络（PMN）和性能恢复网络（PRN）。 PMN使用基本点分类器生成伪点样本，而PRN使用伪点对点分类器进行精细调整，以最大化性能。我们的实验结果表明，PPM比以前的同等标注量的半监督人群计数方法表现更好，并且与最先进的完全监督方法实现了竞争性能。