We show that crowd counting can be viewed as a decomposable point querying process. This formulation enables arbitrary points as input and jointly reasons whether the points are crowd and where they locate. The querying processing, however, raises an underlying problem on the number of necessary querying points. Too few imply underestimation; too many increase computational overhead. To address this dilemma, we introduce a decomposable structure, i.e., the point-query quadtree, and propose a new counting model, termed Point quEry Transformer (PET). PET implements decomposable point querying via data-dependent quadtree splitting, where each querying point could split into four new points when necessary, thus enabling dynamic processing of sparse and dense regions. Such a querying process yields an intuitive, universal modeling of crowd as both the input and output are interpretable and steerable. We demonstrate the applications of PET on a number of crowd-related tasks, including fully-supervised crowd counting and localization, partial annotation learning, and point annotation refinement, and also report state-of-the-art performance. For the first time, we show that a single counting model can address multiple crowd-related tasks across different learning paradigms. Code is available at https://github.com/cxliu0/PET.

我们介绍了一种新的计数模型，称为Point quEry Transformer（PET），它通过数据相关的四叉树分割实现可分解的点查询，使得稀疏和稠密区域的动态处理成为可能。我们展示了PET在众多与人群相关的任务上的应用，并报道了最先进的性能。首次证明了一个单一的计数模型可以处理不同学习范式下的多个与人群相关的任务。

点查询四叉树用于人群计数、定位与更多