聚焦人群计数中局部信息，提升小范围密度识别能力。
Local Information Matters: A Rethink of Crowd Counting
- 用网格窗划分输入，增强模型对局部密度的建模能力。
- 在高密度数据集上MAE降低8.7%，显著提升计数精度。
- 适合关注局部细节的密集人群场景，如活动监控。
本文重新思考人群计数的核心特性：人群中的个体（人头）通常只占图像极小区域。现有方法多采用通用视觉任务的骨干网络，追求大感受野，却忽视了局部信息的重要性。为此，我们提出新设计原则：强化模型的局部建模能力，并据此构建名为LIMM的模型。核心创新包括：将输入划分为网格窗口的分块设计，以及基于窗口的对比学习机制，以增强模型对局部密度差异的区分能力；同时在模型末端引入全局注意力模块，处理偶发的大尺寸个体。在多个公开数据集上的实验表明，所提模型在局部建模能力上显著提升（例如在JHU-Crowd++高密度子集上MAE降低8.7%），且未牺牲对大个体的计数能力，达到当前最优性能。代码已开源：https://github.com/tianhangpan/LIMM。
原文摘要 · Abstract (English)
The motivation of this paper originates from rethinking an essential characteristic of crowd counting: individuals (heads of humans) in the crowd counting task typically occupy a very small portion of the image. This characteristic has never been the focus of existing works: they typically use the same backbone as other visual tasks and pursue a large receptive field. This drives us to propose a new model design principle of crowd counting: emphasizing local modeling capability of the model. We follow the principle and design a crowd counting model named Local Information Matters Model (LIMM). The main innovation lies in two strategies: a window partitioning design that applies grid windows to the model input, and a window-wise contrastive learning design to enhance the model's ability to distinguish between local density levels. Moreover, a global attention module is applied to the end of the model to handle the occasionally occurring large-sized individuals. Extensive experiments on multiple public datasets illustrate that the proposed model shows a significant improvement in local modeling capability (8.7\% in MAE on the JHU-Crowd++ high-density subset for example), without compromising its ability to count large-sized ones, which achieves state-of-the-art performance. Code is available at: https://github.com/tianhangpan/LIMM.
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